Leonardo Medrano Sandonas

Technische Universität Dresden | TUD · Faculty of Mechanical Engineering

A crucial goal for increasing thermal energy harvesting will be to progress towards atomistic design strategies for smart nanodevices and nanomaterials. This requires the combination of computationally efficient atomistic methodologies with quantum transport based approaches. Here, we review our recent work on this problem, by presenting selected a...

We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to construct a non-linear model for the localized many-body interatomic repulsive energy, which so far has been treated in...

The rational design of molecules with targeted quantum-mechanical (QM) properties requires an advanced understanding of the structure–property/property–property relationships (SPR/PPR) that exist across chemical compound space (CCS). In this work, we analyze these fundamental relationships in the sector of CCS spanned by small (primarily organic) m...

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify chemical compounds with tailored properties. While quantum-mechanical (QM) methods, coupled with machine learning, already offer a direct mapping from 3D molecular structures to their properties, effective methodologies for the in...

The GEMS method enables molecular dynamics simulations of large heterogeneous systems at ab initio quality.

Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key step toward this goal has been made with the GEMS approach to biomolecular dynamics [Sci. Adv. 10, eadn4397 (2024)...

Understanding how solvation affects structure-property and property-property relationships of drug-like molecules is crucial for de novo design, as most relevant reactions occur in aqueous environments. We have thus performed an exhaustive analysis of the recently proposed Aquamarine dataset to gain insights into the effect of solvent-molecule inte...

We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have perfor...

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights int...

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean:50.9), and containing up to 54 (mean:28.2) non-hydrogen atoms. To gain insights into...

We develop a quantum embedding method that enables accurate and efficient treatment of interactions between molecules and an environment, while explicitly including many-body correlations. The molecule is composed of classical nuclei and quantum electrons, whereas the environment is modeled via charged quantum harmonic oscillators. We construct a g...

Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural netw...

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify novel chemical compounds and materials with desired properties for a specific application. In particular, quantum-mechanical (QM) methods combined with machine learning (ML) techniques have accelerated the estimation of accurate m...

Understanding correlations - or lack thereof - between molecular properties is crucial for enabling fast and accurate molecular design strategies. In this contribution, we explore the relation between two key quantities describing the electronic structure and chemical properties of molecular systems: the energy gap between the frontier orbitals and...

Predictive modeling for toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural net...

Understanding correlations - or lack thereof - between molecular properties is crucial for enabling fast and accurate molecular design strategies. In this contribution, we explore the relation between two key quantities describing the electronic structure and chemical properties of molecular systems: the energy gap between the frontier orbitals and...

We develop a quantum embedding method that enables accurate and efficient treatment of interactions between molecules and an environment, while explicitly including many-body correlations. The molecule is composed of classical nuclei and quantum electrons, whereas the environment is modeled via charged quantum harmonic oscillators. We construct a g...

By employing a mechanically controllable break junction technique, we have realized an ideal single molecular linear actuator based on dithienylethene (DTE) based molecular architecture, which undergoes reversible photothermal isomerization when subjected to UV irradiation under ambient conditions. As a result, open form (compressed, UV OFF) and cl...

Predictive modeling for toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural net...

By employing a mechanically controllable break junction technique, we have realized an ideal single molecular linear actuator based on dithienylethene (DTE) based molecular architecture, which undergoes reversible photothermal isomerization when subjected to UV irradiation under ambient conditions. As a result, Open form (compressed, UV OFF) and Cl...

Two-dimensional materials have great potential for applications as high-performance electronic devices and efficient thermal rectificators. Among them, pristine phosphorene, a single layer of black phosphorus, has shown promising properties such as ultrahigh charge mobility, a tunable band gap, and mechanical flexibility. However, the introduction...

Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atoms. For larger systems, efficient, but much less reliable empirical force fields are used. Recently,...

Rational design of molecules with targeted properties requires understanding quantum-mechanical (QM) structure-property/property-property relationships (SPR/PPR) across chemical compound space. We analyze these relationships using the QM7-X dataset---which includes multiple QM properties for ~4.2 M equilibrium and non-equilibrium structures of smal...

Rational design of molecules with targeted properties requires understanding quantum-mechanical (QM) structure-property/property-property relationships (SPR/PPR) across chemical compound space. We analyze these relationships using the QM7-X dataset---which includes multiple QM properties for ~4.2 M equilibrium and non-equilibrium structures of smal...

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for ≈4.2 million equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable...

Thermal management is a current global challenge that must be exhaustively addressed. We propose the design of a nanoscale phononic analogue of the Ranque-Hilsch vortex tube in which heat flowing at a given temperature is split into two different streams going to the two ends of the device inducing a temperature asymmetry. Our nanoscale prototype c...

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for $\approx$ 4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)sta...

We combine density-functional tight-binding (DFTB) with deep tensor neural networks (DTNN) to maximize the strengths of both approaches in predicting structural, energetic, and vibrational molecular properties. The DTNN is used to learn a non-linear model for the localized many-body interatomic repulsive energy, which so far has been treated in an...

The Molecular Quantum Cellular Automata paradigm (m-QCA) offers a promising alternative framework to current CMOS implementations. A crucial aspect for implementing this technology concerns the construction of a device which effectively controls intramolecular charge-transfer processes. Tentative experimental implementations have been developed in...

With the advances in fabrication of materials with feature sizes at the order of nanometers, it has been possible to alter their thermal transport properties dramatically. Miniaturization of device size increases the power density in general, hence faster electronics require better thermal transport, whereas better thermoelectric applications requi...

Recent progress in nanostructuring of materials opens up possibilities to achieve more efficient thermoelectric devices. Nanofilms, nanowires, and nanorings may show increased phonon scattering while keeping good electron transport, two of the basic ingredients for designing more efficient thermoelectric systems. Here, we argue that graphene nanori...

We report how the electron transport through a solid-state metal/Gly-Gly-His tripeptide (GGH) monolayer/metal junction and the metal/GGH work function are modified by the GGH complexation with Cu2+ ions. Conducting AFM is used to measure the current-voltage histograms. The work function is characterized by combining macroscopic Kelvin probe and Kel...

A fundamental problem for thermal energy harvesting is the development of atomistic design strategies for smart nano-devices and nano-materials that can be used to selectively transmit heat. We carry out here an extensive computational study demonstrating that heterogeneous molecular junctions, consisting of molecular wires bridging two different n...

We report how the electron transport through a solid-state metal/Gly-Gly-His tripeptide (GGH) monolayer/metal junction and the metal/GGH work function are modified by the GGH complexation with Cu2+ ions. Conducting AFM is used to measure the current-voltage histograms. The work function is characterized by combining macroscopic Kelvin probe and Kel...

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Despite the uniquely high thermal conductivity of graphene is well known, the exploitation of graphene into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the individual nanosheets. A fascinating yet experimentally challenging route to enhance thermal conductance at contacts between graphen...

Despite the uniquely high thermal conductivity of graphene is well known, the exploitation of graphene nanosheets into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the individual nanosheets. A fascinating yet experimentally challenging route to enhance thermal conductance at contacts betw...

BNC heteronanotubes are promising materials for the design of nanoscale thermoelectric devices. In particular, the structural BN doping pattern can be exploited to control the electrical and thermal transport properties of BNC nanostructures. We here address the thermoelectric transport properties of (6,6)-BNC heteronanotubes with helical and horiz...

Over the past two decades, controlling thermal transport properties at the nanoscale has become more and more relevant. This is mostly motivated by the need of developing novel energy-harvesting techniques based on thermoelectricity and the necessity to control the heat dissipation in semiconductor devices. In this field, two major research lines c...

Mass transport through graphene is receiving increasing attention due to the potential for molecular sieving. Experimental studies are mostly limited to the translocation of protons, ions, and water molecules, and results for larger molecules through graphene are rare. Here, we perform controlled radical polymerization with surface-anchored self-as...

Phonons play a major role for the performance of nanoscale devices and, conse- quently, a detailed understanding of phonon dynamics is required. Using an auxiliary-mode approach, which has successfully been applied for the case of electrons, we develop a new method to numerically describe time-dependent phonon transport. This method allows one to g...

Controlling and improving the interfacial thermal conductance between graphene nanosheets plays a crucial role with respect to the preparation of highly thermal conductive nanomaterials and devices. In the present work, we investigate the effect of interfacial thermal conductance on molecule chains working as thermal bridge between two adjacent gra...

The integrity of phonon transport properties of large graphene (linear and curved) grain boundaries (GBs) is investigated under the influence of structural and dynamical disorder. To do this, density functional tight-binding (DFTB) method is combined with atomistic Green's function technique. The results show that curved GBs have lower thermal cond...

The need to control the electronic properties of hydroxyl-terminated substrates such as Si/SiO2 or Indium-Tin oxide (ITO) is of great importance in both electronics and optoelectronics device applications. Specifically, the relevant electronic properties are the work function (WF) and the electron affinity (EA) of the substrate, which can be tailor...

Copper ions play a major role in biological processes. Abnormal Cu2+ ions concentrations are associated with various diseases, hence, can be used as diagnostic target. Monitoring copper ion is currently performed by non-portable, expensive and complicated to use equipment. We present a label free and a highly sensitive electrochemical ion-detecting...

Controlling and improving the interfacial thermal conductance between graphene nanosheets plays a crucial role with respect to the preparation of highly thermal conductive nanomaterials and devices. In the present work, we investigate the effect in terms of interfacial thermal conductance of molecule chains working as thermal bridge between two adj...

The existence of a disorder-induced metal-insulator transition (MIT) has been proved in cooled silver and copper nanoparticles by using level spacing statistics. Nanoparticles are obtained by employing molecular dynamics simulations. Results show that structural disorder is not strong enough to affect their electronic character, and it remains in t...

Novel two-dimensional (2D) materials show unusual physical properties which combined with strain engineering open up the possibility of new potential device applications in nanoelectronics. In particular, transport properties have been found to be very sensitive to applied strain. In the present work, using a density-functional based tight-binding...

The mechanical response of patterned graphene nanoribbons (GNRs) with a width less than 100 nm was studied in-situ using quantitative tensile testing in a transmission electron microscope (TEM). A high degree of crystallinity was confirmed for patterned nanoribbons before and after the in-situ experiment by selected area electron diffraction (SAED)...

Two-dimensional semiconductor materials with puckered structure offer a novel playground to implement nanoscale thermoelectric, electronic, and optoelectronic devices with improved functionality. Using a combination of electronic structure approaches and Green's function based techniques, we address the thermoelectric performance of phosphorene, ar...

Thermoelectric effect enables direct conversion between thermal and electrical energy and provide an alternative route for power generation and refrigeration. Hereby it is important to find materials with a high thermoelectric performance. In this sense, in the present work, we study the behavior of the thermoelectric properties of functionalized g...

Phononics in two-dimensional (2D) materials is an emergent field with a high potential impact from the basic as well as applied research points of view. Hereby it is crucial to provide strategies to control heat flow via atomic-scale engineering of the materials. In this study, thermal diodes made of single layer \ce{MoS2} nanoribbons are investiga...

An alternative method to determine the critical cooling rate of materials has been developed by explaining the size and cooling rate dependences of physical properties of metallic nanoparticles through the scaling theory. This method has been applied to silver and copper nanoparticles which have been obtained by molecular dynamics simulations. The...

An alternative method to determine the critical cooling rate of materials has been developed by explaining the size and cooling rate dependences of physical properties of metallic nanoparticles through the scaling theory. This method has been applied to silver and copper nanoparticles which have been obtained by molecular dynamics simulations. The...