Work

Predicting Electrolyte Conductivity Directly from Molecular-Level Interactions

Year: 2022

Type: article

Abstract: Ionic conductivity is a fundamental property to evaluate an electrolyte blend’s per- formance. Though it is a macroscopic quantity, ionic conductivity is derived from molecular interactions including the stoichiometry and relative population of ion solvation clusters. There is growing interest in constructing low-cost, high-throughput electrolyte design frameworks that can evaluate the properties of novel electrolyte blends in silico. A framework that can accurately predict ionic conductivity without empirical fitting would therefore be transformative. This study outlines such an approach built on a chemical physics formalism with parameters computed by classical molecular dynamic (MD) simulations. Quantitative and qualitative agreements are achieved for strong and weak electrolytes, respectively. This work provides the basis for conductivity predictions for novel battery electrolyte systems, facilitating in silico screening before synthesis.

Source: ACS Energy Letters

DOI: 10.1021/acsenergylett.2c01947

Authors: Yumin Zhang, Imanuel Bier, Venkatasubramanian Viswanathan

Institution: Carnegie Mellon University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: U.S. Department of Energy, Advanced Research Projects Agency - Energy, Advanced Research Projects Agency

Awards: G1467515376, G6553639979, THIS COOPERATIVE AGREEMENT IS AWARDED TO CARNEGIE MELLON UNIVERSITY FOR A PROJECT ENTITLED “HIGH-FIDELITY ACCELERATED DESIGN OF HIGH-PERFORMANCE ELECTROCHEMICAL SYSTEMS”. THIS AWARD IS BEING MADE IN ACCORDANCE WITH FOA NUMBER DE-FOA-0002107 DIFFERENTIATE AND APPLICATION CONTROL NUMBER 2107-1548.