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

Karandashev, KonstantinFaculty of Physics, University of Vienna, Kolingasse 14-16, AT-1090 Wien, Austria (författare)

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

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.

Weinreich, JanFaculty of Physics, University of Vienna, Kolingasse 14-16, AT-1090 Wien, Austria (författare)
Heinen, StefanVector Institute for Artificial Intelligence, Toronto, M5S 1M1 Ontario, Canada (författare)
Arismendi Arrieta, Daniel JoseUppsala universitet,Strukturkemi(Swepub:uu)danar352 (författare)
Falk von Rudorff, GuidoDepartment of Chemistry, University Kassel, Heinrich-Plett-Str.40, 34132 Kassel, Germany;Center for Interdisciplinary Nanostructure Science and Technology (CINSaT), Heinrich-Plett-Straße 40, 34132 Kassel, Germany (författare)
Hermansson, Kersti,ProfessorUppsala universitet,Strukturkemi(Swepub:uu)kerstihs (författare)
von Lilienfeld, O. AnatoleVector Institute for Artificial Intelligence, Toronto, M5S 1M1 Ontario, Canada;Departments of Chemistry, Materials Science and Engineering, and Physics, University of Toronto, St. George Campus, Toronto, M5S 1A1 Ontario, Canada;Machine Learning Group, Technische Universität Berlin and Institute for the Foundations of Learning and Data, 10587 Berlin, Germany (författare)
Faculty of Physics, University of Vienna, Kolingasse 14-16, AT-1090 Wien, AustriaVector Institute for Artificial Intelligence, Toronto, M5S 1M1 Ontario, Canada (creator_code:org_t)

Ingår i:Journal of Chemical Theory and Computation: American Chemical Society (ACS)19:23, s. 8861-88701549-96181549-9626

Av författaren/redakt...: Karandashev, Kon ...; Weinreich, Jan; Heinen, Stefan; Arismendi Arriet ...; Falk von Rudorff ...; Hermansson, Kers ...; visa fler...; von Lilienfeld, ...; visa färre...

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