Alex M Maldonado

Alex M Maldonado
University of Pittsburgh | Pitt · Department of Biological Sciences

Doctor of Philosophy

About

15
Publications
2,239
Reads
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134
Citations
Introduction
I develop novel ways to accurately and quickly predict how complicated chemical reactions occur in solvents using state-of-the-art quantum chemistry and machine learning.
Education
August 2018 - April 2022
University of Pittsburgh
Field of study
  • Chemical Engineering
September 2013 - April 2018
Western Michigan University
Field of study
  • Chemical Engineering

Publications

Publications (15)
Preprint
Full-text available
Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms, but the employed global descriptor limits transferability to ensembles of molecules. Many-body expansions (MBEs) should provide a rigorous procedure for size-transferable GDML by training model...
Preprint
Full-text available
Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms, but the employed global descriptor limits transferability to ensembles of molecules. Many-body expansions (MBEs) should provide a rigorous procedure for size-transferable GDML by training model...
Article
Full-text available
Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms, but the employed global descriptor limits transferability to ensembles of molecules. Many-body expansions (MBEs) should provide a rigorous procedure for size-transferable GDML by training model...
Article
Bonding energies play an essential role in describing the relative stability of molecules in chemical space. Therefore, methods employed to search chemical space need to capture the bonding behavior for a wide range of molecules, including radicals. In this work, we investigate the ability of quantum alchemy to capture the bonding behavior of hypot...
Article
Due to the sheer size of chemical and materials space, high-throughput computational screening thereof will require the development of new computational methods that are accurate, efficient, and transferable. These methods need to be applicable to electron configurations beyond ground states. To this end, we have systematically studied the applicab...
Preprint
Full-text available
Bonding energies are key for the relative stability of molecules in chemical space. Therefore methods employed to search for relevant molecules in chemical space need to capture the bonding behavior for a wide range of molecules, including radicals. In this work, we investigate the ability of quantum alchemy to do so for exploring hypothetical chem...
Preprint
Full-text available
Due to the sheer size of chemical and materials space, high throughput computational screening thereof will require the development of new computational methods that are accurate, efficient, and transferable. These methods need to be applicable to electron configurations beyond ground states. To this end, we have systematically studied the applicab...
Article
Computational quantum chemistry promises to help guide the design of catalysts that are more sustainable and economical. This Feature Article gives a tutorial overview of how our group accounts for the thermodynamics and kinetics of chemical reactions in complex environments. We start with explanations of how to include environmental contributions...
Article
Full-text available
Computational quantum chemistry provides fundamental chemical and physical insights into solvated reaction mechanisms across many areas of chemistry, especially in homogeneous and heterogeneous renewable energy catalysis. Such reactions may depend on explicit interactions with ions and solvent molecules that are nontrivial to characterize. Rigorous...
Presentation
Modeling solvation effects in computational chemistry often requires fully explicit treatments (i.e. molecular dynamics) to accurately and reliably capture solute-solvent interactions. Unfortunately, such methods require pre-parameterized force fields or many quantum mechanics calculations that can incur a prohibitively high computational cost. Rec...
Preprint
div>Computational quantum chemistry modeling provides fundamental chemical and physical insights into solvated reaction mechanisms across many areas of chemistry, especially in homogeneous and heterogeneous renewable energy catalysis. Such reactions may depend on explicit interactions with ions and solvent molecules that are non-trivial to characte...
Article
Full-text available
Mixed solvents (i.e., binary or higher order mixtures of ionic or nonionic liquids) play crucial roles in chemical syntheses, separations, and electrochemical devices because they can be tuned for specific reactions and applications. Apart from fully explicit solvation treatments that can be difficult to parameterize or computationally expensive, t...
Article
We provide a critical overview of progress and challenges in computationally modeling multistep reaction mechanisms relevant for catalysis and electrocatalysis. We first discuss how the chemical and materials space of energetically efficient catalysis can be explored with computational chemistry. Since reactions for renewable energy catalysis can i...
Conference Paper
Full-text available
Immunodiagnostics play a critical role in disease diagnosis and monitoring. Further advancement of immunoassays will extend the reach of medical technology to allow testing in the field with inexpensive and disposable point-of-use biosensors that respond to target molecule(s) (antigens) in test solutions by a change in one or more of the biosensor'...

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