A materials discovery framework based on conditional generative models applied to the design of polymer electrolytes
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.
Authors
- Weike Ye (ORCID: https://orcid.org/0000-0002-9541-7006)
- Ha-Kyung Kwon (ORCID: https://orcid.org/0000-0002-9351-4806)
- X. L. Lei (ORCID: https://orcid.org/0000-0002-2333-9205)
- Linda Hung (ORCID: https://orcid.org/0000-0002-1578-6152)
- Zhenze Yang (ORCID: https://orcid.org/0000-0002-6427-6757)
- Arash Khajeh (ORCID: https://orcid.org/0000-0002-9061-9370)
- Daniel Schweigert (ORCID: https://orcid.org/0009-0007-2520-4746)
Institutions
- Toyota Motor Corporation (Switzerland) (CH)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1039/d4dd00293h
- Citations
- 16
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.87
Funders
- Massachusetts Institute of Technology
- Toyota Research Institute