Titel
Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design
Autor*in
Stefan Heinen
Vector Institute for Artificial Intelligence
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Abstract
Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science but also a very difficult one due to the vast number of possible molecular systems. We propose an evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favorable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely, minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO–LUMO gap; optimization was carried out over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) data sets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus feasible only thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches.
Stichwort
Covalent bondingKinetic parametersMolecular structureMoleculesOptimization
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
https://phaidra.univie.ac.at/o:2044728
Erschienen in
Titel
Journal of Chemical Theory and Computation
Band
19
Ausgabe
23
ISSN
1549-9618
Erscheinungsdatum
2023
Seitenanfang
8861
Seitenende
8870
Verlag
American Chemical Society (ACS)
Projektnummer
957189 – European Union (all programmes)
Projektnummer
957213 – European Union (all programmes)
Projektnummer
772834 – European Union (all programmes)
Erscheinungsdatum
2023
Zugänglichkeit
Rechteangabe
© 2023 The Authors

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