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Questions related to Computational Chemistry
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I'm looking for recommendations on software or tools that provide the most accurate and customizable 3D plotting for QSPR (Quantitative Structure-Property Relationship) models. The tool should allow for high-quality visualization of molecular descriptors and structure-property relationships, with options for customization, interactivity, and integration with computational chemistry or statistical analysis frameworks. Any suggestions on the best tools for this purpose?
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Pradeepa A. : Usually, I avoid using 3D plots. However, if a third dimension is required, I have found Origin Pro to provide the necessary tools for such plots. This program can also generate movies of 3D plots so that the observer can see the plot in various orientations. For generating molecular descriptors, models based on them, and statistical analysis of the models, Simulations Plus ADMET Predictor/Modeler is excellent. For customization, interactivity, and integration with other software, R and python are splendid tools. Finally, another interesting approach is the KNIME analytics platform.
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I optimized the structure of Pd Acetate Trimer using HF/STO-3G and the B3LYP/LANL2DZ without any solvent. The optimization was successful without any negative frequency.
Then I added solvent IEFPCM/DCE to the system. This causes the optimization to show error link 9999. How Do I fix this problem ?
I have tried using the last optimized geometry and used that for next calculation but every time its showing same error.
I am adding the drive links to the files herewith:
It has the input geometries and log files for both without solvent and with solvent calculations.
Can anyone please help ?
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I have the optimized geometry of an organic molecule and also its TD-SCF energy. The excited state wavelength predicted by Gaussian approximately matches the experimental calculations.
I have optimized the donor molecule on a model structure of pristine graphene to check whether it is binding or not. I want to find out how the UV-Vis spectrum of donor will change once it binds with graphene.
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Hello, Ayan Bera
Did you compute the TD for the optimized donor molecule -graphene? It should show shifts in the peaks compared to the donor molecule.
You may need to increase the number of roots.
You can try "#p td(nstates=6) <DFT_functional/Basis_set> <other keywords."
I hope this is useful.
Regards,
Dilawar
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Hi Everyone,
I have some questions regarding geometry optimizations for excited states, and I hope someone can clarify them for me.
Question:
When performing calculations involving excited states in Gaussian, I should use TD-SCF methods, correct? In that case, I need to include the "td" keyword in my route line; otherwise, the calculations will be performed in the ground state.
But, specifically, for molecules with a net spin of 1, if I am interested in optimizing the geometry for T1 and T2, I should proceed as follows:
  • For T1, I should change the multiplicity to 3 and not include the "td" keyword (e.g., `# opt freq L.O.T`).
  • For T2, I should include "td=root=1" and set the multiplicity to 3 (e.g., `# opt freq td=(root=1) L.O.T`).
Is this specifically for molecules with a net spin of 1 because T1 is essentially their ground state on triplet?
Additionally, should all my calculations follow this route line format if I want to calculate the geometry for excited states S1, S2, S3, etc.?
  • (e.g., `# opt freq td=(root=1,2,3…) L.O.T`).
Thank you!
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Calculating the T1 using DFT with 3 multiplicity, lacks rigor compared to TD-DFT because it treats T1​ as a triplet ground state rather than an excited state.
This approach doesn´t account for the S0→T1​ electronic transition. As a result, it provides the minimum energy of the triplet potential energy surface but does not yield the true excitation energy. In contrast, TD-DFT explicitly models T1 as an excited state derived from S0 being more accurate and consistent description.
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Dear computational chemists;
I need to plot the electron density difference between the ground state and excited state using gaussian or AIMALL. I have the optimized geometry for both states.
Thank you
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Dear Ms. Manal, you may use Multiwfn program:
If you decide to try on it, in the manual section 4.18 you'll find ground-to-excited state analysis.
Best, Pablo
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Hello,
I have several .xyz files (each composed of several molecules) and I'd like to find an automated way to visualize and save them.
I'd appreciate any guidance.
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Hi Ashly,
Have you found a way to do this?
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I am currently working on obtaining the transition state for the deprotonation of a carbocation by histidine. Initially, I froze the bond lengths between the histidine nitrogen and the proton, as well as between the proton and the methyl group being deprotonated. This approach resulted in a single imaginary frequency of over -1000 cm⁻¹ (please see the attached files: 1_input and 1_output).
I then attempted to release these constraints and recalculate the transition state. I have tried various keyword combinations, including calcfc, calcall, scf=qc, and IOP(1/8=1) in conjunction with the level of theory # opt=(ts,noeigen) freq mpw1pw91/6-31+g(d,p). However, the structure did not converge. The energy oscillates uniformly without reaching a minimum (attached is a graph showing this behavior).
Could you provide any suggestions on how to address this issue ?
Thank you for your time and assistance.
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The method you use to calculate a TS is correct, Opt=(TS,calcfc,noeigen). However, it is important to have a good approximation to the TS. You should first perform a scanning of how energy changes when methyl is deprotonated (see attached). To do that, do a scan job:
# opt=modredundant plus other keywords
.../...
.../...
B 1 2 S 10 0.10
This is only an example, replace 1 and 2 with the atom label of your molecule, S means scan type job, 10 -0.10 means ten steps with an increase of 0.10 angstroms. You need to adjust these parameters to your problem.
After the scan finishes, select the highest point and run TS optimization.
Hope this is helpful.
Best, Pablo
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Good morning everyone,
While running fqha.x executable, the output is printed with "File containing the dos". I need phonon DOS to run fqha.x. here, I considered the phonon DOS file, which is created as one of the outputs while running matdyn.x executable. Please, let me know where am i doing wrong.
Thank you,
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You need to remove few lines in the generated .dos file. If you need more info please message me. i am unable to message you.
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Dear all,
I'm trying to perform a QM/MM calculation which makes use of DLPNO-CCSD(T)-F12/D for the high level, and XBT for the low level. My input looks like this for ORCA:
!QM/XTB DLPNO-CCSD(T)-F12/D cc-pvtz-F12 cc-pvtz-F12-CABS cc-pvtz-F12-MP2fit DefGrid2 SmallPrint Opt NUMGRAD
%qmmm
QMAtoms {0:12} end
end
* xyz 0 1
coordinates for moleclues
*
I have been trying to get ORCA to run this calculation, but it keeps crashing after 3 hrs with the following error:
--------------------------------- --------------------
FINAL SINGLE POINT ENERGY (L-QM2) 0.000000000000 (Wavefunction not fully converged!)
--------------------------------- --------------------
[file orca_main/main_util_tools.cpp, line 549]: Cannot open gbw file
[file orca_main/main_util_tools.cpp, line 549]: Cannot open gbw file
However, the gbw file is there. So I was wondering what I could do to remove this error?
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try adding accelerators for SCF:
! KDIIS SOSCF LSHIFT
well and maybe increase the number of steps in SCF
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I want to simulate polymer in water for that I have confusion in reduce units according my understanding of reduce units if we perform simulation in reduce units means we are make a generalize model because we set sigma , Ellison, mass and other bonding parameters equal to one means we are simulating not real model.
It's like we are doing simulation of ball and spring model.
My confusion is regarding parameter that is equal to one or not for all atoms?
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Your understanding of reduced units in molecular dynamics simulations is on the right track. Reduced units are a way to scale the physical properties of a system so that certain parameters are set to unity, which can simplify calculations and make the simulation more general. Here’s a clearer explanation of reduced units and how they apply to your polymer in water simulation:
Reduced Units:
In reduced units, the following parameters are often set to 1:
  • Length (σ): The length unit is typically set to the size of a particle, which could be the van der Waals radius or another relevant length scale.
  • Energy (ε): The energy unit is often set to the strength of the pairwise Lennard-Jones interaction between particles.
  • Mass (m): The mass unit is set to the mass of a particle in the system.
These units are defined relative to the system you are studying. For example, in a Lennard-Jones fluid, σ might be the distance at which the interparticle potential is zero, and ε might be the depth of the potential well.
Are All Parameters Equal to One?
No, not all parameters are equal to one. Only the base units (length, energy, and mass) are set to one. Other parameters, such as bond lengths, angles, charges, and force constants, are scaled relative to these base units. Here’s how it works for your polymer in water system:
  • Bond lengths and angles: These are scaled relative to the length unit (σ). For example, if a bond length in your polymer is 0.2 nm, and σ is defined as 0.1 nm, then the bond length in reduced units would be 2σ.
  • Charges: These are scaled relative to the square root of the energy unit (ε) divided by the length unit (σ). This ensures that the electrostatic interaction energy has the correct dimensions.
  • Force constants: For bonded interactions like harmonic bonds and angles, the force constants are scaled relative to the energy unit (ε) and the length or angle unit.
Simulating a Real Model:
When you perform a simulation in reduced units, you are still simulating a real model. The advantage is that the simulation becomes more general and can be applied to a wide range of systems with similar interactions. The parameters you use (like σ, ε, and m) are chosen to reflect the physical properties of the actual system you are studying.
Polymer in Water:
For a polymer in water, you would typically define your reduced units based on the properties of the solvent (water) and the polymer. For example:
  • Length (σ): Could be set to the oxygen-oxygen van der Waals distance in water.
  • Energy (ε): Could be set to the strength of the Lennard-Jones interaction between water molecules.
  • Mass (m): Could be set to the mass of a water molecule.
Then, the polymer’s properties would be scaled accordingly. This does not mean you are simulating a ball and spring model; rather, it’s a way to abstract the physical properties to a set of units that makes the simulation more manageable and computationally efficient.
In summary, using reduced units does not mean you are simulating a non-real model. It is a way to standardize the system so that the simulation can be applied broadly while still reflecting the physical properties of the real system. The choice of what parameters to set to one and how to scale the others depends on the specifics of the system you are simulating.
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🚀 Introducing OpenQP: A new open-source platform for quantum chemical collaboration, now live at [OpenQP on GitHub](https://github.com/Open-Quantum-Platform/openqp). Discover innovative features like the Mixed-Reference Spin-Flip Time-Dependent Density Functional Theory (MRSF-TDDFT) and more in our latest manuscript: [Read here](https://doi.org/10.26434/chemrxiv-2024-k846p).
👏 Kudos to incredible people: Vladimir, Konstantin, Igor, Jingbai, and many others whose dedication made this possible!
OpenQP (Open Quantum Platform) tackles sustainability and interoperability challenges in computational chemistry. The platform offers a range of autonomous modules for quantum chemical theories, including energy and gradient calculations for HF, DFT, TDDFT, SF-TDDFT, and MRSF-TDDFT, facilitating seamless integration with third-party software.
### 🔍 Key Features of OpenQP
- Autonomous modules for quantum chemistry theories, enhancing interoperability.
- Ground and excited state properties computed using [MRSF-TDDFT](https://doi.org/10.1021/acs.jpclett.3c02296).
- Nonadiabatic coupling via [TLF Technology](https://doi.org/10.1021/acs.jpclett.1c00932) using MRSF-TDDFT.
- Innovative DTCAM series [exchange-correlation functionals](https://doi.org/10.1021/acs.jctc.4c00640).
### 🚀 What’s Next?
- Spin-Orbit Coupling via [Relativistic MRSF-TDDFT](https://doi.org/10.1021/acs.jctc.2c01036).
- Ionization Potential/Electron Affinity with [EKT-MRSF-TDDFT](https://doi.org/10.1021/acs.jpclett.1c02494).
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Very interesting. I hope that I can join in.
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Hi all,
I'm a master student working with computational molecular fragmentation and docking.
I did a virtual screening using autodock vina of some thousends of molecules, and I'm wondering if there's a solution to fragment the molecules based on the interactions observed on the docking poses. In the end I want obtain the smiles of the fragment, the AA the fragment is interacting and the type of interaction.
Is there any methods to do this?
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Hi everyone, I'm currently working on an autodock4 and I've received those warning in my running process. I don't know how to fix or how it affects to my final results. Attached below are my dpf file and cmd warning.
Thanks for your advices.
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Hello dear researchers
Please I have a problem with pdos using qe
I have calculated dos, and I get it, but when I calculated pdos using projwfc.x, I got 0 values for all orbitals!!!!
I used paw pps and I don't know why this happen?
Please if someone can help me or met this problem before!?
I put some files attached here: dos.in, projwfc.in and some orbitals files (all pdos files are set to 0, you can see that all the columns are 0)!!
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Thanks Professor Merve Özcan
I am working on vanadate materials, I think they are so complex, they take a lot of time in calculations
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I'm trying to calculate the electronic energy transfer of the excited state using gaussian.
This is the command line
#p td=(singlets,nstates=6,root=1) rb3lyp/6-311g density=current eet(fragment=2)
the run keeps crash with syntax error in eet(fragment=2)
How can I solve this?
Thank you
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Dear Manar, I think these references can be fundamental to helping you calculate the electronic energy transfer of the excited state using Gaussian.
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Hello dear colleagues and professors
Please what is the recommended type of pseudopotentials for QE? ultrasoft or norm conserving??
And from where get all types of pseudos (us, nc, pbe, pbesol,......)
Tanks in advance
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1. pseudopotentials - Quantum Espresso (quantum-espresso.org)
You can use various types of pseudopotentials according to your compound and research. Use different potentials and perform test calculations. Then try to compare the results with reference papers.
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.
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Hello Fellow researchers, I would like to use GROMACS to check DNA-short peptide interaction to chose the best peptide to synthesize in the wet lab. By short, I mean around 15-20 residues. Is it possible?
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Hello dear colleagues and professors
Please what is the recommended type of pseudopotentials for QE? ultrasoft or norm conserving??
And from where get all types of pseudos (us, nc, pbe, pbesol,......)
Tanks in advance
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Pramod Gawal Thank you so much sir for your response
I will use them and compare
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Hey everyone!
I can't run any calculations on Gaussian 03 and 09. It stopped when running the l302.exe module on my new computer.
Processor: 13th Gen Intel(R) Core(TM) i9-13900HX 2.20 GHz
RAM 32.0 GB
On other computers, it runs fine with the same software.
Thank you very much!!
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A few of things I’d suggest trying.
First, set the amount of memory values a %mem. About 70% of your total memory should be good for parallel execution.’
second, use #P to get extended output - the error codes you get back are more informative, and most visualisation programs require extended output anyway.
i might also try this job without specifying the chk file; take the last geometry from your output if a job keels over. I’ve found all kinds of problems with specifying checkpoints, including jobs that fail ruining them for future use, as well as path issues. My experience of Gaussian is that it’s not often worth the effort of manually managing chk files.
Also, as noted, check, or fully specify, the path of your scratch directory (and chk files if you want to use them).
last question - the job is in that state fir days; is the appropriate link still running? Or is the entire process dead?
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Hello dear researchers, I hope you are doing well.
I want to ask you a question. I have a unit cell with 24 atoms (4 A, 4 B and 16 X) and I want to substitute X atom by another atom (for example changing one X by other element).
My question is, can I substitute it within the unit cell without making supercell?? or should I make a supercell??
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A novel variable into game theory could indeed be a valuable contribution to the field, potentially offering new insights or enhancing existing models. Here’s how you might conceptualize and present such a "present" to science:
Conceptualizing a Novel Variable in Game Theory
  1. Identify a Gap or Enhancement Opportunity:Review existing game theory models and literature to identify areas where a new variable could provide deeper insights or improve predictive accuracy. Consider aspects such as behavioral psychology, environmental factors, or dynamic variables that current models may not fully capture.
  2. Define the Novel Variable:Clearly define the new variable and its potential impact on decision-making within game scenarios. Examples could include introducing time-dependent variables, emotional states, resource constraints, or adaptive learning capabilities of players.
  3. Integration with Existing Models:Demonstrate how the novel variable can be integrated into existing game theory frameworks (e.g., Nash equilibrium, evolutionary game theory) to refine predictions or outcomes. Illustrate through mathematical formulations or simulations how the variable interacts with traditional game theory concepts.
Presenting Your Contribution
  1. Research Paper or Article:Write a research paper detailing the rationale, methodology, and findings related to introducing the novel variable. Structure the paper to include background on game theory, a clear description of the variable, its theoretical implications, and practical applications.
  2. Simulation or Case Study:Conduct simulations or case studies to illustrate the impact of the novel variable in various game scenarios. Provide quantitative results and qualitative insights to support your theoretical propositions.
  3. Discussion and Implications:Discuss how the novel variable addresses existing limitations in game theory and enhances the understanding of decision-making processes. Explore potential real-world applications and policy implications based on your findings.
  4. Peer Review and Publication:Submit your paper to peer-reviewed journals specializing in game theory, economics, or behavioral sciences. Respond to reviewer feedback to strengthen the rigor and relevance of your contribution.
  5. Presentation and Dissemination:Present your findings at academic conferences, workshops, or seminars to solicit feedback and engage with other researchers in the field. Consider creating visual aids or demonstrations to effectively communicate the novelty and impact of your variable.
Example Scenario: Introducing Emotional State as a Variable
  • Variable Definition: Introduce emotional state (e.g., happiness, stress) as a dynamic variable influencing decision-making in game interactions.
  • Model Integration: Modify traditional game theory models to incorporate emotional states as parameters affecting strategy choices and outcomes.
  • Applications: Explore how emotional variability among players affects cooperation, competition, and equilibrium states in social dilemmas or economic negotiations.
By presenting a novel variable in game theory, you contribute not only to theoretical advancements but also potentially to practical applications in fields ranging from economics to social sciences. Your "present" to science could pave the way for deeper insights into human behavior and decision-making dynamics.
3.5
Doping/Substitution of an element in QE
ChatGPT
In the context of quantum mechanics and materials science, "QE" typically refers to Quantum Espresso, which is an integrated suite of open-source computer codes for electronic-structure calculations and materials modeling. Doping or substitution of an element within Quantum Espresso involves simulating the introduction of foreign atoms (dopants) into a material to study its electronic and structural properties. Here’s how this process is generally approached:
Doping/Substitution in Quantum Espresso (QE)
  1. Setup and Structure Initialization:Input File Preparation: Define the crystal structure of the material of interest using Quantum Espresso's input format (typically based on atomic positions and unit cell parameters). Specify Dopant Positions: Identify the specific sites where dopant atoms will replace or occupy lattice positions within the crystal structure.
  2. Electronic Structure Calculation:Density Functional Theory (DFT): Utilize DFT calculations to compute the electronic structure and ground-state properties of the doped material. Exchange-Correlation Functional: Choose an appropriate functional (e.g., LDA, GGA) to describe the electron exchange and correlation interactions accurately. Pseudopotentials: Select suitable pseudopotentials for the elements involved to approximate the atomic cores and reduce computational complexity.
  3. Simulation Parameters and Convergence:K-Points and Energy Cutoff: Define the Brillouin zone sampling (k-points) and energy cutoff for plane-wave expansions to achieve converged results. Relaxation and Optimization: Optionally, perform structural relaxation to minimize atomic forces and optimize the lattice parameters around the doped atoms.
  4. Analysis and Visualization:Electronic Properties: Analyze the band structure, density of states (DOS), and charge distribution to understand how doping affects the material's electronic properties. Structural Properties: Evaluate changes in lattice parameters, bond lengths, and atomic positions due to doping.
  5. Interpretation and Applications:Dopant Effects: Assess how the introduced dopants alter the material's behavior, such as its conductivity, magnetic properties, or catalytic activity. Comparison with Experimental Data: Validate computational findings against experimental observations to refine theoretical models and predictions.
Example Scenario: Doping Silicon with Phosphorus
  • Objective: Study the effects of phosphorus doping on silicon's electronic properties using Quantum Espresso.
  • Steps:Prepare the crystal structure of silicon (Si) and specify the substitutional positions for phosphorus (P) atoms. Perform DFT calculations with appropriate pseudopotentials and exchange-correlation functionals. Calculate the band structure and DOS to analyze how P doping modifies Si's electronic bandgap and carrier concentration. Compare results with experimental data on doped silicon to validate computational predictions and insights.
Impression
Doping or substitution of elements within Quantum Espresso allows researchers to explore how the introduction of foreign atoms influences the electronic and structural characteristics of materials. This approach is fundamental in materials science for understanding doping mechanisms, predicting material behaviors, and designing novel materials with tailored properties for various applications.
3.5
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Hi Collegues,
To calculate entropy (T*S) and Gibbs (G) free energy of electroreductions using computational hydrogen electrode (CHE) model (according to this equation, G = E + ZPE - TS + U), can we use vaspkit after frequency calculation by VASP? I have attached some examples they mentioned on the vaspkit.
Thank you.
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yep. You can use vaspkit to output ZPE and entropy. But you first need to compute vibrational frequency (you can search setup in INCAR to calculate this one), then get freq from OUTCAR.
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When I optimize the structure, there is a difference in energy with and without LORBIT tags under exactly the same conditions.
Does anyone know why there is this difference?
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In the case with LORBIT set to true, you'll add an additional term to the total Hamiltonian. This term couples the spin and lattice (angular momentum operator), and causes the energy differences you've observed.
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Hello dear researchers, I hope you are doing well.
I want to ask you a question. I have a unit cell with 24 atoms (4 A, 4 B and 16 X) and I want to substitute X atom by another atom (for example changing one X by other element).
My question is, can I substitute it within the unit cell without making supercell?? or should I make a supercell??
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Hello! When substituting an atom within a unit cell in quantum espresso (QE), whether you need to create a supercell depends on various factors, including the size of the substituted atom compared to the original X atom and the specific properties you're investigating.
In general, if the substituted atom significantly alters the unit cell's dimensions or introduces strain, creating a supercell may be necessary to maintain the system's integrity. Additionally, if the substitution leads to a change in the system's symmetry, a supercell may be required to accommodate these changes.
However, if the substituted atom is of a similar size and chemical nature as the original X atom, and the substitution does not significantly affect the unit cell's properties, you may be able to perform the substitution within the existing unit cell without creating a supercell.
It's essential to consider the specific requirements of your simulation and the desired accuracy of your results when deciding whether to use a supercell or perform the substitution within the unit cell. Experimentation and careful analysis of the system's behavior can help guide your decision-making process.
Ultimately, the best approach may vary depending on the specific characteristics of your system and the goals of your research. Feel free to provide more details if you need further assistance!
Best regards,
Sandeep
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Hi there,
I understand one can use the mouse to click the control panel to show disulphide bonds on the protein, but just out of curiosity, is there any command line that is able to do the same, which is to show the disulphide bonds on a certain object?
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select SSbond, CYS/SG and bound_to CYS/SG
show sticks, SSbond
You can further refine the selection by specifying the object, chain, range of residues etc.
example:
#load antibody
fetch 1igt
color slate, chain B+D
color pink, chain A+C
color white, hetatm
#show hinge disulphides
select hingeSS, (/1igt/B/B/CYS/SG and bound_to /1igt/D/D/CYS/SG) or (/1igt/D/D/CYS/SG and bound_to /1igt/B/B/CYS/SG)
show sticks, hingeSS
color yellow, hingeSS
#since this shows only the bond between the sulfur atoms, you may want to expand the selection to the entire cystein residue
show sticks, br. hingeSS
color yellow, br. hingeSS
Similarely, you can selectively display the disulfide bonds between light and heavy chain and the various intradomain disulfide bonds
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I want to do NBO analysis for a molecule in the excited state [the first singlet state]. I used the following command line,
#P td rb3lyp/ 6-311g Cis=(read, root= 1) pop=nboread
but the job crash with the following error
No CIS info on Chk file
How can I solve the error??
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I disagree, the basis set you just mentioned is unbalanced and will yield unreliable results. Including diffuse functions without polarization ones will heavily underestimate short-range interactions (i.e. chemical bonds) in favor of long-range effects, which is meaningless. At a bare minimum use 6-311+G(d) if you want to use a triple-zeta basis set with diffuse functions.
Now, td=(nstates=1,root=1) can be used for excited-state geometry optimizations, vibrational frequencies, population analyses… but not to obtain a wave function. output=wfn (or wfx) will still refer to the ground state.
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Hello,
I run open-shell DLPNO-CCSD(T) calculations using ORCA package for some molecule-radical systems. Some systems show the T1 diagnostic value of ~0.025 which as far as I am aware, means that the systems show multiconfigurational character.
I have run stability check on the UHF reference wave function by including the
%scf stabperform true end
line in the input file
The results show that the wave function is stable. Indeed, the CCSD iteration converges slowly, taking about 75 iterations using default ORCA setting.
I use the quasi-restricted orbital for the DLPNO-CCSD(T) calculations
Is there any way to get a reliable reference wave function so that the T1 diagnostic is below 0.025?
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No, that wouldn't make sense. You have to stay within the same method, otherwise errors that normally self-compensate will pile up, so if you switch e.g. to CASSCF or CASPT2 for the educt, the same needs to be done for the product.
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In a groundbreaking development shared within a Facebook group, the barriers between GROMACS and Windows have been successfully dismantled. The GROMACS community is elated to announce the triumphant porting of the renowned molecular dynamics simulation software to the Windows platform. This achievement not only broadens the user base but also opens up new possibilities for collaboration and exploration in the realms of computational chemistry and bioinformatics.
Traditionally confined to Unix-based systems like Ubuntu, GROMACS has been a staple in molecular dynamics simulations. However, a recent announcement within a Facebook group has unveiled a transformative milestone – the successful porting of GROMACS to the Windows platform. This breakthrough, which allows users to run GROMACS simulations on Windows via the command prompt, signifies a significant step towards increased accessibility and inclusivity.
Installation GROMACS on Windows:
The key to this newfound accessibility lies in the GROMACS installer tailored for Windows, available for download at the following link:
This installer simplifies the process, enabling users to seamlessly integrate GROMACS into their Windows environment and execute simulations directly through the command prompt. This is installed version of Gromacs 2020.3, both CPU and GPU version is available. In GPU version, you need to install latest NVIDIA CUDA driver and in case of windows 11, you need to put a dll file in the bin folder. https://www.dll4free.com/cufft64_10.dll.html
Technical Insight:
The successful porting of GROMACS to Windows raises questions about the technical intricacies involved in this process. While the Facebook announcement doesn't delve into the specifics, the installer provided indicates a streamlined approach to bring GROMACS functionality to Windows users. Researchers and enthusiasts keen on exploring the technical aspects of this achievement are encouraged to investigate the installer and potentially contribute to the ongoing development.
Performance Evaluation:
One of the critical aspects of this porting endeavor is the performance of GROMACS on the Windows platform. Researchers are encouraged to share their experiences and insights regarding the performance of GROMACS simulations on Windows, comparing it to the traditional Unix environment. Identifying any optimizations or challenges encountered during this process will contribute to the collective knowledge of the community.
Community Collaboration:
The Facebook group announcement not only celebrates the successful porting of GROMACS to Windows but also invites the community to actively engage in the ongoing discussions. Users are encouraged to share their experiences, feedback, and potential improvements related to running GROMACS on Windows. The provided link serves as a hub for collaboration, fostering a sense of community among researchers, developers, and enthusiasts interested in GROMACS on Windows.
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I appreciate your curiosity regarding the advantages of running Gromacs on Windows. While both Windows and Ubuntu are viable platforms, the choice often depends on user preference, familiarity, and specific requirements. Windows, being widely used, provides accessibility for a broader user base, especially those accustomed to its interface. Additionally, some researchers may find it convenient to integrate Gromacs seamlessly into their existing Windows workflows. It's essential to recognize that the suitability of an operating system for simulations can vary based on individual needs and project specifications.
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I am trying to calculate the NTOs of a specific td-dft calculation. I am using this input:
%oldchk=state9oh.chk
%chk=state9.chk
%nproc=8
#p m062x/6-31+G(d,p) Geom=AllCheck Guess=(Read) iop(9/40=4) Density=(Check,Transition=9) Pop=(Minimal,NTO,SaveNTO)
-3 1
--Blank line--
But, i am getting this error:
This type of calculation cannot be archived.
How can i solve it? The oldchk file is in the same folder, with the correct name.
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B. R. Ramachandran Thank you very much!
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Hello, Can anybody assist me in expanding the number of cores using gaussian 9.0 program. I have multiprocessor system with Intel® Core™ i5-1135G7 (up to 4.2 GHz with Intel® Turbo Boost Technology, 8 MB L3 cache, 4 cores) and 8GB RAM. I am using Avogadro and Gaussian, so kindly assist me where to put the commands?
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For those still interested, a new version of GaussMem is available for calculating the amount of memory required by Gaussian calculations as a function of the type of calculation and the number of processors. It can be freely downloaded at the program page:
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I've been having some trouble with the output I've obtained from my calculations: I've tried to optimize three excited states from the same molecule. All of them reached convergence (and in all three cases the .log file states that the calculation has terminated normally). But the problem is that I can't load the .fchk file in the MO's visualization menu. When I try to do it, the following error pops up:
'SCUtil_ConnectionGFCHK::Parse_GFCHK()
Missing or bad data: MM charges
Line number: 54'
I'm fairly new to the software and to computational chemistry, sorry if it's too much of a basic question. Should I try to maybe change something manually in the .fchk file?
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Hi Juan. Try to change "MM charges" to "MM-charges". It worked for me.
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Hello everyone!
I'm about to start a research grant on energy storage materials. The first task is to replicate the results of a paper where they used DFT and MD calculations (with VASP) to simulate the interaction of a gas adsorbing onto a Li slab. (Stephan L. Koch, Journal of Power Sources, 2015, DOI: 10.1016/j.jpowsour.2015.07.027, Pages 150-161)
I have little experience with DFT calculations (although with Gaussian), but none with MD and VASP. Additionally, my university doesn't have a license for VASP.
Could you suggest valid alternatives to VASP and provide some teaching materials on how to use the software for these types of calculations?
Thank you very much!
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For DFT calculations you could use Quantum Expresso or CASTEP software, which are free to use (CASTEP is free for academic use).
CASTEP is also capable of running AIMD, but if you want only MD simulations i would suggest CHARMM or DL_POLY (https://www.scd.stfc.ac.uk/Pages/DL_POLY.aspx), which are also free for academic use.
Hope this helps!
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From you experience, which DFT method and basis sets are recommended for organic molecules optimization using Gaussian and provide a trusted energy and proprieties ( e.g UV-vis spectra)
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Choosing methods and basis for organic molecules depends upon the nature of the problem one is studying. You may find that for the DFT, basis set dependency is less for a particular functional. But if one is targeting the excited state ( may or may not use solvent), Then the choice of DFT functional becomes quite essential. Review articles are available where benchmarks are performed for various types of molecules for different excited properties; in your case, it could be the absorption spectrum.
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I have three transition states corresponding to a catalytic cycle, and there are 3 non-bonding interactions involved in them. Are there any tools available in Gaussian 16 that could quantify the strength and nature of these interactions?
Also, can Wiberg Bond order index be used for determining the above?
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Dear Sobitri,
If you wish to qualitatively compare the non-covalent interactions in multiple transition states, using an NCI plot is the best approach.
Please refer to an article from Prof. Sunoj's group: https://pubs.acs.org/doi/10.1021/acs.orglett.7b00890
To understand how to use it, please consult the actual paper: https://pubs.acs.org/doi/10.1021/ct100641a
However, for quantitative comparisons, employ the AIM method (refer to the same paper or recent papers from Prof. Sunoj). To my knowledge, the software they use is a paid version. You can try the Multiwfn software for the same purpose. Please visit the program's website for more details.
--
Best Regards
Vipin
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Dear Researchers,
There is a sentence which is frequently encountered in computational chemistry field as "this functional highly suffers from self interaction error". Could you please clarify what is the meaning of SIE and where does it originate from? Any comment is highly appreciated.
Best regards,
Saeed
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Self interaction arises from including all occupied orbital densities in the Hartree potential of any single-particle approach to many-body quantum mechanics. This can allow the computational cost of the single-particle method to be reduced significantly, but introduces a spurious contribution to the Hartree potential for every occupied single-particle orbital, where each orbital feels the Hartree potential of its own density.
Advanced single-particle methods can remove this spurious 'self-interaction' by including the exchange interaction, such as Hartree-Fock. Within Kohn-Sham theory, as Jürgen Weippert explains, the exact exchange-correlation (xc) potential removes self-interaction completely. However, in practice, owing to the need to approximate the xc part of the Kohn-Sham potential, the full self-interaction is not removed, leading to the infamous self-interaction error. Hence, the size of this error corresponds to the reliability of the approximation to the xc potential.
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[INFO ] Running calculations on normal system...
[INFO ] Beginning GB calculations with /home/bio/anaconda3/envs/gmxMMPBSA/bin/sander
[INFO ] calculating complex contribution...
100%|##########| 101/101 [elapsed: 08:08 remaining: 00:00]
[INFO ] calculating receptor contribution...
100%|##########| 101/101 [elapsed: 08:16 remaining: 00:00]
[INFO ] calculating ligand contribution...
100%|##########| 101/101 [elapsed: 00:02 remaining: 00:00]
[INFO ] Beginning PB calculations with /home/bio/anaconda3/envs/gmxMMPBSA/bin/sander
[INFO ] calculating complex contribution...
0%| | 0/101 [elapsed: 00:00 remaining: ?][ERROR ] CalcError
/home/bio/anaconda3/envs/gmxMMPBSA/bin/sander failed with prmtop COM.prmtop!
If you are using sander and PB calculation, check the *.mdout files to get the sander error
Check the gmx_MMPBSA.log file to report the problem.
File "/home/bio/anaconda3/envs/gmxMMPBSA/bin/gmx_MMPBSA", line 8, in <module>
sys.exit(gmxmmpbsa())
File "/home/bio/anaconda3/envs/gmxMMPBSA/lib/python3.9/site-packages/GMXMMPBSA/app.py", line 101, in gmxmmpbsa
app.run_mmpbsa()
File "/home/bio/anaconda3/envs/gmxMMPBSA/lib/python3.9/site-packages/GMXMMPBSA/main.py", line 205, in run_mmpbsa
self.calc_list.run(rank, self.stdout)
File "/home/bio/anaconda3/envs/gmxMMPBSA/lib/python3.9/site-packages/GMXMMPBSA/calculation.py", line 142, in run
calc.run(rank, stdout=stdout, stderr=stderr)
File "/home/bio/anaconda3/envs/gmxMMPBSA/lib/python3.9/site-packages/GMXMMPBSA/calculation.py", line 625, in run
GMXMMPBSA_ERROR('%s failed with prmtop %s!\n\t' % (self.program, self.prmtop) +
File "/home/bio/anaconda3/envs/gmxMMPBSA/lib/python3.9/site-packages/GMXMMPBSA/exceptions.py", line 171, in __init__
raise exc('\n\n' + msg + '\n\nCheck the gmx_MMPBSA.log file to report the problem.')
CalcError:
/home/bio/anaconda3/envs/gmxMMPBSA/bin/sander failed with prmtop COM.prmtop!
If you are using sander and PB calculation, check the *.mdout files to get the sander error
Check the gmx_MMPBSA.log file to report the problem.
Error occurred on rank 6.
Exiting. All files have been retained.
Abort(1) on node 6 (rank 6 in comm 0): application called MPI_Abort(MPI_COMM_WORLD, 1) - process 6
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Okay! I'll do this.
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I am calculating the oniom energy for a complex, receptor only and ligand only to calculate the free binding energy of the ligand of interest. I've seen different papers that do get similar kcal/mol calculations to their respective docking experiments but wanted an opinion on if it truly makes sense to do so. Both methods are taking in completely different things when forming their calculations. I figured each would only be able to be compared relative to each other? Example. Ligands can only be compared to each other via docking alone and ligands used in oniom can only be compared to each other through oniom.
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Some factors to consider when comparing results from different methods:
1. Methodological differences: Docking and free energy calculations using ONIOM are based on different underlying principles and assumptions. Docking typically predicts the binding pose and affinity of a ligand to a receptor based on scoring functions and geometric complementarity. ONIOM calculations, on the other hand, use a combination of quantum mechanical (QM) and molecular mechanics (MM) methods to describe the electronic structure and energetics of a system. These differences in approach can lead to variations in the absolute values obtained.
2. Relative comparisons: As you correctly mentioned, the primary utility of comparing results from different methods lies in their relative comparisons. The goal is to assess the relative binding affinities or trends between different ligands or receptor-ligand complexes within the same method. For example, if docking experiments consistently rank a set of ligands in a certain order of affinity, and the ONIOM calculations show a similar trend, it provides confidence in the relative comparison between ligands using both methods.
3. Calibration and validation: To establish a meaningful correlation between different methods, it is often necessary to calibrate and validate the results against experimental data or reference datasets. This can involve benchmarking a set of compounds with known binding affinities and comparing the relative rankings obtained from different methods. Calibration can help adjust the scaling factors or parameters to improve the agreement between methods and experimental results.
4. Limitations and assumptions: It's important to consider the limitations and assumptions of each method. Docking, for instance, may not fully capture the precise energetics of the binding process, while ONIOM calculations may have limitations in terms of the chosen level of theory, basis set, or representation of the system. Understanding these limitations can guide the interpretation and comparison of results.
Hope it helps:credit AI
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  • When to use B3LYP and CAM B3LYP? I also want to know why it is called B3LYP and CAM B3LYP?
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B3LYP (Becke, 3-parameter, Lee-Yang-Parr) is a popular hybrid functional that combines the local density approximation (LDA) with the gradient-corrected exchange and correlation functionals. It works well for a wide range of systems but can struggle with predicting properties related to charge transfer and long-range interactions. Also, B3LYP, while widely used and successful in many cases, does not explicitly include dispersion corrections (also known as van der Waals forces).
CAM-B3LYP, on the other hand, includes a correction for long-range interactions. The "CAM" stands for "Counterpoise Corrected for Adsorption Model," and it's particularly useful when dealing with molecules that involve noncovalent interactions, like dispersion forces. This correction accounts for the overestimation of interaction energies that can occur when the basis sets of two interacting fragments overlap.
In a nutshell, if you're working with systems where intermolecular interactions play a significant role, CAM-B3LYP might be the way to go. Otherwise, good old B3LYP should serve you well.
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Hello Everyone
I did a NBO calculation using %CHOOSE keylist . I used different syntax but every time  I got a similar error.
"Keyword for orbital type is not LONE, BOND, or 3CBOND"
Please, Suggest me what may be possible cause of error. If it is syntax error, how it can be solved.
Thank You
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Hello,
By using the NBO 3.1 program, you need to add the keyword "alpha" even if you're studying a closed-shell system. As in the following example:
$nbo
$end
$CHOOSE
alpha
BOND S 1 2
D 1 2
S 1 6
S 1 7
S 2 3
S 2 8
S 3 4
D 3 4
S 3 9
S 4 5
S 4 10
S 5 6
D 5 6
S 5 11
S 6 12 END
$END
My best regards
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Computational chemistry
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Vacuum padding is a method of creating a vacuum or reducing air pressure inside a container or packaging. It's commonly used to extend the shelf life of food, protect delicate items, save space, and shape materials in various industries. The process involves removing air and sealing the container to maintain reduced pressure.
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Computational chemistry
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K-point sampling is a technique used in electronic structure calculations, particularly in the context of periodic systems like crystals. These calculations are often based on methods such as density functional theory (DFT) and are used to study the electronic properties of materials.
In a periodic system, the electronic wavefunctions repeat over the crystal lattice, and the Brillouin zone is a fundamental concept in understanding the electronic structure. To perform calculations efficiently, a finite set of special points in the Brillouin zone, known as k-points, is sampled rather than calculating the electronic structure at every point in the Brillouin zone.
The choice of k-points is crucial, and it affects the accuracy and efficiency of the calculation. The goal is to accurately represent the electronic structure while minimizing computational resources. The k-point grid is like a mesh or grid of points in the Brillouin zone where calculations are performed.
Determining the appropriate k-point sampling involves finding a balance between accuracy and computational cost. Generally, denser k-point grids provide more accurate results but require more computational resources. Various methods, such as Monkhorst-Pack grids or special symmetry considerations, are used to determine the distribution of k-points in the Brillouin zone.
In practical terms, software packages for electronic structure calculations often have automated routines to determine suitable k-point grids based on the user's input or default settings. In addition, convergence tests can be performed by executing calculations with various k-point grids to ensure that the results are consistent and converge as the grid becomes denser.
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In the a molecule I am working on, there is a certain O-H bond. I wanted to study the variation of energy on changing the bond length, for that I did a relax-scan computation on g16. I have generated the output where in the .log file I have 50 different structures, each with their energies.
As my next step, I want to calculate vertical excitation energy for each of the 50 structures. I can do it manually by switching to each structure from the window shown in the attachment and use the GUI to generate a .com file of energy calculation for each of them.
But this process gets too tedious and eventually impossible as the number of structures in my scan increase to >100. Is there any way to automate this using some script? Any help will be appreciated
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You can run simultaneously both relax-scan and td-dft calculations.
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I encountered the calculations in the research article titled " ." However, I'm having difficulty comprehending the procedure used to calculate ΔrG20 and ΔrG30, particularly when incorporating solvent model adjustments. My confusion lies in the precise method for calculating the correction terms when considering the solvent model. I've included the worksheet I employed in an attempt to replicate the results showcased in Table 3 of the mentioned article.
In Table 3 of the paper, it is stated that ΔrG20 equals −44.433 kJ/mol. Strikingly, my calculations yield a value of -26.707 kJ/mol for ΔrG20. Furthermore, for ΔrG30, the paper indicates a value of −28.424 kJ/mol, whereas my calculations result inΔrG30 = -23.355 kJ/mol. Regarding the computed redox potential (E0) using the B3LYP/6-31G(d,p) level of theory, the article reports a value of 0.682 V, but my calculations yield 0.616 V.
I would be extremely grateful for any guidance or assistance you could provide in resolving these discrepancies or any other methods (not the use of isodesmic reactions) to calculate the redox potentials of aforementioned systems using DFT calculations.
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First, you need to determine the thermodynamic cycle.
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I want to determine if there are acid-base reaction or just hydrogen bond interaction between 2 organic molecules by Gaussian software. Can anyone help me on this problem? Thank you in advance.
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You should optimize the complex with both reactants in a favorable position / orientation for the possible proton transfer to occur, with an appropriate level of theory and basis set. The final position of the hydrogen could provide an answer to your problem: if it stays on the acid it is H-bonding, and if it is transferred to the basis it is an acid-base reaction.
your results can be more accurate if you take the solvent into account (at least as a polarizable continuum) and if you compensate for basis set superposition error (either using a fairly large basis set or using the counterpoise scheme).
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Dear Colleagues,
I am new to the computational chemistry, and I am primarily interested in the exchange energy calculations for polynuclear metal complexes.
Is the broken-symmetry DFT approach applicable to binuclear gadolinium complexes? If so, which combination of the functional / basis set / other parameters would be appropriate to yield a convergent SCF for both HS and BS states?
How would an ORCA 5.0 input file look like for such a calculation? A routine BS-DFT calculation, which works well for copper(ii) complexes, does not yield a convergent SCF in the case of Gd(III) ions.
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Broken symmetry DFT calculations in Gd-dimer if both the Gd are in +3 oxidation state is absolutely fine since it has isotropic spin ground state, that is octet and orbital singlet. There are many references as for now I can recommend https://pubs.rsc.org/en/content/articlehtml/2021/dt/d1dt01770e
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I want to teach computational chemistry to elementary students and I should explain this difference with basic words.
""DFT 🆚 MD""
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You could tell them that MD concerns itself with simulating how atoms and molecules move, while DFT concerns itself with the energy and properties that electrons determine for fixed atomic positions. Avoid terms like "dynamics", "functional" or "electronic structure". These might be too complicated for elementary school children. Use "motion", "energy" even "forces" instead.
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I came across a manuscript under review titled “Neighbor List Artifacts in Molecular Dynamics Simulations”. see "Neighbor List Artifacts in Molecular Dynamics Simulations | Theoretical and Computational Chemistry | ChemRxiv | Cambridge Open Engage" This study claimed that many non-expert users rely on default values for key inputs, which can lead to deformation in some cases, such as large membranes simulations. The study also suggested that most simulations suffer from inappropriate parameters for outer cutoff, ri and nstlist, resulting in missing long-range Lennard-Jones interactions. I would appreciate any comments and suggestions.
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Dear Kanski
Would you please explain about parameters setting in your simulations? for example rc and r in a protein-ligand simulation?
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Hello,
I am trying to run a calculation using the HF/LANL2DZ level of theory, and am interested in visualizing the canonical orbitals and the basis functions. The molecule of interest is pentagonal planar (D5h point group), and I would like to choose the coordinate system such that the py basis functions on the terminal atoms are pointing towards the center of the molecule.
Does g09 support this? What keywords would I have to add to the route section to achieve this?
Thank you very much!
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Adam Matěj Thank you for your response- The project I am working on is for students to better visualize the quantitative calculations by comparing it to qualitative calculations taught in inorganic chemistry courses.
I am comparing qualitative results calculated from group theory to quantitative results from a computational calculation for a planar D5h symmetric molecule.
In the qualitative calculation, the py basis functions were oriented pointing towards the z axis to emphasize the symmetry. I would like to reflect that in the quantitative calculation so I can make a cleaner comparison.
I am using AOMix to print the coefficients from the computational calculation, however at the moment since the basis functions are not oriented as desired, I must do some trigonometry which is rather tedious. I also use the visualization software iqmol to visualize the basis functions, so having them oriented to emphasize symmetry would be useful.
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Hello,
We want to continue one of our previous simulations and add one more molecule into the system. We used 53a6 force field files from ATB server for our previous simulations since we started it many years ago. However, currently, when we submit a molecule to the ATB server, it always outputs the G54A7FF force field files. Could you please tell me how can I get 53a6 files from the ATB server?
Any help would be much appreciated!
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As of my knowledge cutoff in September 2021, the ATB (Automated Topology Builder) server provided force field files for the Gromacs G54A7FF force field, which is an updated version of the Gromos 53a6 force field. The G54A7FF force field includes improvements and refinements over the original Gromos 53a6 force field.
If you specifically need the Gromos 53a6 force field files for your simulations, you may face challenges obtaining them directly from the ATB server since it primarily provides the G54A7FF force field files.
However, there are a few potential options to consider:
  1. Local Backups: If you have performed simulations using the Gromos 53a6 force field in the past, you should check your local backups or archives for the necessary force field files. If you have saved the required files from your previous simulations, you can use those files for the continuation of your simulations.
  2. Contact ATB Support: If you have a specific reason for using the Gromos 53a6 force field files and cannot find them in your backups, you can reach out to the support team or administrators of the ATB server. They might be able to provide you with more information or assistance in accessing the older Gromos 53a6 force field files.
  3. Use G54A7FF Force Field: Alternatively, you can consider using the G54A7FF force field files provided by the ATB server for your simulations. Although the force field is updated, it may still be suitable for your research needs. Be sure to review the differences between G54A7FF and Gromos 53a6 force fields and evaluate if the updated force field is acceptable for your specific study.
  4. Consult with Experts: If your research or project requires the Gromos 53a6 force field for specific reasons, you may consider consulting with experts in your field or other researchers who have experience with this force field. They might be able to provide insights or guidance on using the Gromos 53a6 force field in your simulations.
Please note that the availability of force field files on the ATB server might change over time, so it's essential to check the latest information and options available on the ATB website or contact their support team for the most up-to-date details.
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I am investigating disulfide radicals of the type in Hall et al (2014) doi:10.1021/ja500087m, and I am having trouble rationalizing the results I am getting in the optimizations using Gaussian. Obviously the lowest energy is found when the charge is symmetrically distributed, but I want to investigate the geometry and energy is the charge is localized on one sulfur. If that doesn't seem to make sense chemically, trust me I know, I'm working a hunch.
I have run a CASSCF calculation but the calculation still determines the symmetrical distribution to be better and gives that result. I have tried making one of the sulfurs a separate charged fragment and the initial guess gives me a good result, but once I run the optimization it all blends.
Is there any way to get the software to give the lowest-energy-possible-if-this-one-condition-is-met?
Thanks in advance for any ideas!
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Dear Max, you may try Constrained DFT in the case of charge localized in one fragment. Let's say we have complex A / A, with C-DFT you can optimize A / A+.
On the other hand, charge separation is different, let's say A+ / A-
In this case, I used to move a molecular orbital from one fragment to the other one, thus creating a hole (A+) in one fragment and an electron in the other (A-).
Hope this is useful.
Best, Pablo
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Hello, I'm a very new user of Gaussian 16. So, I started doing optimization and frequency of MIL-53(Fe) [construct with 3 atoms Fe and 4 bdc linker] cluster model using MN15L/genecp. The calculation was finished and I got inp.log. How do I know is the optimization and frequency calculation was success? And how do I get these data: (1) The optimized structure, (2) Thermodynamics (like enthalpy, Gibbs free energy, etc).
Please, advise me.
Thank you.
R. S.
This is my input
%mem=32Gb
%nproc=12
#p MN15L/genecp opt freq=NoRaman
test
-1 2
.....
****
O 0
6-31+G(d)
****
C H 0
6-31G(d)
****
Fe (lanl2ldz)
****
FE
FE-ECP (lanl2dz)
****
Note: Actually I wanna calculate the energy adsorption of CO2 using the energy that I got after opt and freq calculation with this formula: Eads= Esystem - (EMOFs + ECO2).
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Hi,
You can find all the related information in the log file.
In a successful optimization the following command will print the line:
grep "Otimization completed" log-file
If the optimized structure is a stationary one, then the following command will print that information:
grep "Stationary point found" log-file
After the frequency calculation the thermodynamic data is printed in the log file following the "Thermochemistry" line. Search for "Zero-point correction".
To get the optimized structure, you can use GAUSSIAN newzmat command:
newzmat -ichk -opdb -N 9999 chk-file out-file
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I'm running CHARMM36 simulations of a peptide on GROMACS and I need its N-terminus to be capped with an acetyl group. I checked the “aminoacids.n.tdb” file, but there is no entry for acetylated N-terminus. How can I find/make a terminus?
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Dear all, have you solved the problem? I have the same problem as Harry, could you please clarify how you solve this problem? Thanks much!
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advanced technology, AI, computational chemistry, supramolecular chemistry
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Aside from the already mentioned usage for computational chemistry, there is also potential for all experimental fields, but there it is crucial that the experimentators
  1. provide their data in a standardized form, otherwise the AI's algorithms learn wrong relationships and draw systematically false conclusions.
  2. link their data with suitable metadata, in the best case via a reasonable database. This can be a simple spreadsheet for sample sets of limited complication, for more sophisticated sets a legitimate SQL database will be more helpful.
While I'm not using AI on my data right now, I am eager to keep my data in an "AI-ready" structure so that when we decide to have a bot crawl through our data at some point, maybe a simpler AI of the year 2024 will already find something useful and we won't have to wait for a "careless operator handling" AI of the year 2030.
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Can domeone advise which course, software to take to learn computational chemistry??
website as well will be appreciated
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I guess you are asking for something practical, here an excellent guide:
J. B. Foresman and Æ Frisch, Exploring Chemistry with Electronic Structure Methods, 3rd ed., Gaussian, Inc.: Wallingford, CT, 2015.
Also, don't forget YouTube Channels by ADF:
or Schrodinger:
Or theory plus practice:
Essentials of Computational Chemistry. Theories and Models. Second Edition. Christopher J. Cramer.
See also Cramer's channel on YouTube:
or 'must read' theory:
Modern quantum chemistry introduction to advanced electronic structure theory /. Attila Szabo
or more fundamental courses in quantum mechanics: those given free on YouTube by 'Standford Online' or 'MIT OpenCourseWare'.
And finally, if you don't know where to start, you may ask ChatGPT for something you are interested to learn, we are already in the age of AI.
Best, Pablo
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I have an allylic organic compound that I’ll be deprotonating to make an allylic anion. This anion will have the lone pair delocalizing on 2 different (chemically non equivalent) carbons. I’m trying to rationalize the regioselectivity. Which carbon will be reacting with my electrophile? And why?
I want to do a DFT calculation using Gaussian software to find out which carbon will react? Maybe by calculating the HOMO orbitals energy of the 2 different carbons each carrying the lone pair in 2 separate instances.
I’m not an expert on the subject, so any sources or references for studies conducted for examples of this sort would be appreciated.
Thanks.
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Dear Abdulrahman, you probably need to calculate the transition states for the electrophile attack on one carbon and on the other one. TS will tell to you which carbon reacts firts (kinetically faster). Other properties may help, as you mentioned, HOMO or atomic charges, but those values may only tell to you that the reaction is possible in both carbon atoms.
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I will highly appreciate if you can share one for program version 2014 or higher.
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Can anyone share link please
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I've been having trouble with a specific library and have been considering just writing something myself as the calculation isn't so complicated (just getting the energy as a function of a reaction coordinate). As the library uses the huge vasprun.xml file to source the energy it takes quite a while to run (I'm doing the analysis locally) and am thinking about just implementing the analysis manually. Is there a good reason why I shouldn't just take the final electronic energies from the OUTCARs (or OSZICAR) along the path and plot them myself?
Also, why isn't this what the library does? Is it simply because the XML file has so much extra information so they just tend to import it so they don't need to ask the user to copy over the other VASP output files? It seems quite wasteful for my current application (changing some coordinates around some minima, doing some single-point calculations along some paths and then importing the energies).
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The advantage of vasp.xml is that everything is tagged and designed to be machine-readable. ASE will let you read the OUTCAR or the vasprun.xml. I think pymatgen will read either but can get a lot more info from the vasprun.xml. Looks like vaspkit can read both but I'm not sure what information it extracts from each. I'm a bit surprised that your library is very slow at reading the vasprun.xml--in my experience it usually takes less than 30 seconds.
Anyways, if you prefer to read from OUTCAR or OSZICAR, that should be fine. There are already libraries that do this, but it's easy to write your own function, as you say. Just make sure you're reading the version of the energy that you're interested in.
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I have a coronene molecule functionalized with hydroxyl groups. I want to calculate the charge transfer between the carbon and the OH group, using NBO. But i don't understand how can i do it.
I'm using Gaussian09, which has NBO 3.1.
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Sorry, actually never heard about it.
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I've just begun working with DFTB+ and have a set of crystal geometry optimizations completed, but I'm having trouble obtaining the crystal lattice parameters (A, B, C, Alpha, Beta, Gamma) from the output files. The detailed.out file gives the following:
Total lattice derivs
-0.000004329059 0.000013039724 0.000004783995
0.000032586726 0.000005938166 0.000006491755
0.000004334976 0.000001557498 -0.000023773308
But I haven't found anything in the manual, or any other sources on how to convert this matrix to the parameters.
Thank you for your time.
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Conrad,
Thank you very much for your answer! The information was in the dftb_pin.hsd restart file, just like you said.
-Joshua
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Dear Researchers,
Within the supermolecular approach, counterpoise-corrected interaction energy (not binding energy) for a simple complexation A+B--->AB is defined as:
CP-delta_E=E(complex)-E(monomer 1)-E(monomer 2) in which monomer`s geometries are taken from fully optimized geometry of complex. So, deformation energy is excluded. On the other hand, within IQA approach, total energy of a given molecular system is decomposed as E_int_total= sum(E_self)+ sum (E_int (A,B)) in which E_self indicates intra-atomic energies and E_inter(A,B) represents inter-atomic interaction of any pair atoms. Please let me know how CP-delta_E could be related to E_int_total? Or, accurately, what is the relation between these two equations term-by-term? In addition, please let me know how E_self deformation should be evaluated.
In advance, any kind attention and help is highly appreciated.
Sincerely,
Saeed
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@Shahbaz Ahmad
Dear Shahbaz,
Many thanks for your nice comments.
Sincerely,
Saeed
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How to calculate Fermi energy level and work function for ZIF-8 MOF using Materials Studio?
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Calculating the Fermi energy level and work function for ZIF-8 MOF in Materials Studio requires the following steps:
1. Open the ZIF-8 MOF structure in Materials Studio.
2. Perform a geometry optimization to ensure that the structure is relaxed to its minimum energy configuration.
3. Set up a DFT (Density Functional Theory) calculation using the Materials Studio CASTEP module.
4. Define the k-point sampling scheme for the Brillouin zone. A gamma-point sampling scheme (1x1x1) should be sufficient for the ZIF-8 MOF.
5. Select a suitable exchange-correlation functional. Commonly used functionals include PBE, PW91, and BLYP.
6. Specify the basis set. A plane-wave basis set with a cutoff energy of 500 eV should be sufficient.
7. Set up a SCF (Self-Consistent Field) calculation to obtain the electronic ground state.
8. After the SCF calculation is complete, view the electronic density of states (DOS) using the Materials Studio VNL module.
9. Identify the Fermi energy level from the DOS plot, which is the energy level at which there is a gap between the filled and unfilled states.
10. Calculate the work function by subtracting the Fermi energy level from the vacuum level. The vacuum level can be obtained from the DOS plot by identifying the energy level at which there is a significant drop in the electronic density.
Note that these calculations are computationally intensive and may require access to high-performance computing resources. Additionally, the accuracy of the results can be influenced by the choice of exchange-correlation functional, k-point sampling, and basis set.
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I have done a mechanistic study (I am a beginner and passionate about computational chemistry) to find the reaction pathway in Jaguar Schrodinger.
It is a bulky palladium complex and reactants are also bulkier. I have used B3LYP-D3/LANL2DZ. Schrodinger gives final energy in the Gas Phase Energy (Hartree), when I am plotting the pathway through Reaction Profile Viewer it is showing energy up to 280208 kcal/mol at 298.15 K 1 atm. I think I have made some mistakes that's why the energy is going above 2 lacks. Kindly assist me.
Thank You
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Thank you all
As suggested by Alexey, I think the Stoichiometry of reactants could be an issue. Let me check.
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With 12 atoms, it run. But when I increased to 96 atoms, also increasing nbnd, ecutwfc, ecutrho, its showing error:
....
Band Structure calculation
Davidson diagonalization with overlap
c_bands: 3 eigenvalues not converged
c_bands: 2 eigenvalues not converged
c_bands: 1 eigenvalues not converged
c_bands: 3 eigenvalues not converged
c_bands: 1 eigenvalues not converged
...
After that the program stopped. The screenshot and the input file is given as attachment.
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But you cannot do this:
"nbnd: Used 100 (in bands) instead of 544 (which is default) for faster calculation and for testing purpose"
nbnd is the number of bands to be calculated; you might want to increase it, not decrease it. Indeed, it corresponds to the number of states you make available to the electrons in your materials. QE will start allocating the electrons to each band and then probably crash because there are not enough.
Indeed, in your output, you can read
"number of electrons = 640.00
number of Kohn-Sham states= 100"
How can you fit 640 electron in 100 states? The code is going to crash somewhere.
Moreover,
"ecutrho: 400 (scf) to 700 (bands)"
"ecutwfc: 50 (scf) to 100 (bands)"
These two changes do not make much sense. Remember that the quality of the SCF calculation is in the SCF step and parameters. The calculations="bands" is a NON-SCF type of calculation -- it starts by reading the SCF output and builds from there interpolating the missing points. For example, the density, the central piece of information in DFT, in the non-SCF calculation is not changed.
My advise for a band calculation is to copy the SCF input file and then simply change calculation="bands" and the k-point mesh to what you need.
Finally, why do you want to run a band calculation with a supercell? The result ought to be identical to the case with a simpler cell, once you have unfolded the bands. In this respect, the band calculation offers no new insight into the physics of the supercell.
Best regards,
Roberto
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I am using TS BERNY to optimize the transition state guess structure for my sn2 reaction mechanism. However, when I proceeded to do qst3 calculation, I would receive an error link 9999 all the time. What should I do?
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In general, the program failed to localize the TS. I don't use qst3 or qst2, it is much more input preparation to do and it does not have any advantage over opt=ts when you have a good approximation to the TS.
I suggest, what I always do, first to scan the PES:
#P DFT/basis Opt=ModRedundant
0 1
xyz
B 7 21 S 12 0.10
In this example, the bond "B" betwen atoms "7" and "21" is going to be scanned "S" through "12" steps of "0.10" angstroms step size.
Only if you obtain a smooth curve of the scan you'll know you are in the correct pathway, in that case select the structure at the maximum point of the curve and do a TS calculation directly:
#P DFT/basis Opt=(ts,noeigentest,calcfc) freq
If you don't see a smooth curve, then finding the TS becomes readly challenging, or you should look for another way to do the scan PES (other bonds, angles or even dihedrals).
Best, Pablo
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I am new to computational chemistry and I am trying to get the geometry optimization of TiO2 clusters specifically Ti5O10 using DFT methods and currently I am getting problems on how to converge the clusters. I tried using RHF with STO-3G basis set but the clusters are still not fully converge and it always says "The optimization did not converge but reached the maximum number of optimization cycles". Can someone help me how to converge the clusters? I've been doing this for a month now and I still don't know what is the problem.
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Alexey N. Masliy thank you for this recommendation I already tried this and my cluster have already converge thank you so much.
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I'm calculating the energy of all the E isomers of octadecene using Opt+Freq with DFT B3LYP 3-21G. Everything was fine from 1-octadecene to E-5-octadecene, but from E-6-octadecene onwards it wasn't able to optimize the molecule and it started turning out imaginary frequencies. I don't know what changed between 5 and 6, could it be that the hydrocarbon chain is too large to optimize? Does anyone know what could be causing thus and how to correct it? Help is very much appreciated.
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Just an addition to Ismail Badran answer, sometimes geometry optimisation doesn't end in a minimum but in saddle point of 1st order, where the gradient vanishes too. The more complex the structure, the more likely this may happen.
To get a clearer information, which group might be affected, have a look at the normal modes/vibrations and then follow the normal mode.
If you don't have to stick with gaussion, try using the orca quantum chemical software, which has this outlined approach implemented.
And as Ismail Badran mentioned, try use some larger basis set and include dispersion correction. Van-der-Waals forces should not be neglected even for small systems.
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Greeting,
In computational and theoretical chemistry, many basis sets are available.
  • Can anyone explain the main difference between the Pople-style basis sets (3-21G, 6-31G...) and the def2 basis sets (def2-SVP, def2-TZVP...).
  • What basis sets are the most efficient for the DFT optimization of metal complexes?
Any kind attention and assistance are greatly appreciated in advance.
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I believe that the previous answer is a bit glib. There are a host of advocates of Pople basis sets and the answer to your question is more nuanced. The Karlsruhe def2 basis sets were optimized with DFT in mind; the Pople basis sets predate widespread use of DFT. Nevertheless, one can find Pople basis sets that are comparable to the def2 sets:
Both use some confusing naming conventions. The Karlsruhe basis sets may be slightly more efficient for DFT calculations but I doubt that there are huge differences in computational expense as they utilize the same gaussian orbitals. (I have used both sets and did not notice significant differences in execution speed.)
Metals are particularly difficult to compute and generally require polarization and diffuse functions, which are expensive to include. If you have limited computational resources, you might consider leaving out triple zeta orbitals and performing your calculations with double zeta orbitals but more polarization and diffuse functions. You'll have to do some calculations to get an idea as to the effectiveness and achievability, given your resource limitations.
Finally, what have other investigators used in your system? You may observe that the basis sets employed have evolved over time, reflecting the advances in computational power. To publish your results, you will want to be consistent with current usage; otherwise, you will find that reviewers are critical of your results.
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I want to do a simulation on a cluster using mpirun. My problem is that when it runs it takes 5 minutes and it stops and gives me the following error:
"mpirun noticed that process rank 16 with PID 1524 on node cvb-10 exited on signal 9 (killed).
I should mention that the program is running up to the error point. (No problem with the simulation program).
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