Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design

Autor(en)

Konstantin Karandashev, Jan Weinreich, Stefan Heinen, Daniel Jose Arismendi Arrieta, Guido Falk von Rudorff, Kersti Hermansson, O. Anatole von Lilienfeld

Abstrakt

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.

Organisation(en)

Computergestützte Materialphysik

Externe Organisation(en)

Vector Institute for Artificial Intelligence, Uppsala University, Universität Kassel, University of Toronto, Technische Universität Berlin

Journal

Journal of Chemical Theory and Computation

Band

19

Seiten

8861-8870

Anzahl der Seiten

10

ISSN

1549-9618

DOI

https://doi.org/10.48550/arXiv.2307.15563

Publikationsdatum

12-2023

Peer-reviewed

Ja

ÖFOS 2012

103006 Chemische Physik

ASJC Scopus Sachgebiete

Computer Science Applications, Physical and Theoretical Chemistry

Link zum Portal

https://ucrisportal.univie.ac.at/de/publications/4e0c91ee-f477-469f-aabb-8b2d7ec1efe4