Work

Predicting the chemical reactivity of organic materials using a machine-learning approach

Year: 2020

Type: article

Abstract: data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium-oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium-oxygen batteries.

Source: Chemical Science

DOI: 10.1039/d0sc01328e

Authors: Byungju Lee, Jaekyun Yoo, Kisuk Kang

Institutions: Seoul National University, Lawrence Berkeley National Laboratory, Institute of Engineering Research

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Research Foundation, Ministry of Science, ICT and Future Planning, National Research Foundation of Korea