Work

A materials discovery framework based on conditional generative models applied to the design of polymer electrolytes

Year: 2024

Type: article

Abstract: We introduce a computational materials discovery framework that integrates conditional generation, molecular dynamics simulations, evaluation, and feedback components to design polymer electrolytes with improved ionic conductivity.

Source: Digital Discovery

DOI: 10.1039/d4dd00293h

Authors: Arash Khajeh, Xiangyun Lei, Weike Ye, Zhenze Yang, Linda Hung, Daniel Schweigert, Ha-Kyung Kwon

Institutions: Toyota Research Institute, Massachusetts Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Massachusetts Institute of Technology, Toyota Research Institute

OpenAlex ID: W4404984467

FWCI: 2.554

Cites: 73

Cited by: 28

Related to: 10