Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime-Reference-Cited by-同舟云学术

Machine Learning Modeling for Accelerated Battery Materials Design in the Small Data Regime

Author:

Sendek Austin D.¹²^ORCID,Ransom Brandi²^ORCID,Cubuk Ekin D.³^ORCID,Pellouchoud Lenson A.¹,Nanda Jagjit⁴^ORCID,Reed Evan J.²^ORCID

Affiliation:

1. Aionics, Inc. 221 10 ^th Street Evanston WY 82930, USA

2. Department of Materials Science and Engineering Stanford University 496 Lomita Mall Stanford CA 94305, USA

3. Google Research Brain Team 1600 Amphitheater Pkwy. Mountain View CA 94043, USA

4. Chemical Sciences Division Oak Ridge National Laboratory 1 Bethel Valley Road Oak Ridge TN 37831, USA

Funder

National Science Foundation

U.S. Department of Energy

Vehicle Technologies Office

Office of Energy Efficiency and Renewable Energy

Air Force Office of Scientific Research

Publisher

Wiley

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Link

Reference90 articles.

2. G.Berdichevsky Q&A with Gene Berdichevsky CEO of Sila Nanotechnologies 2021.

4. Materials Data Repository Home https://materialsdata.nist.gov/(accessed: December 2021).

5. Home | ICSD https://icsd.products.fiz-karlsruhe.de/(accessed: December 2021).

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