Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Author:

Zhang Hengrui1ORCID,Lai Tianxing2ORCID,Chen Jie1ORCID,Manthiram Arumugam2ORCID,Rondinelli James M.1ORCID,Chen Wei1ORCID

Affiliation:

1. Northwestern University

2. The University of Texas at Austin

Abstract

Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024

Funder

National Science Foundation

Division of Materials Research

U.S. Department of Energy

Office of Basic Energy Sciences

Division of Materials Science and Engineering

Ryan Graduate Fellowship

Publisher

American Physical Society (APS)

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