Work

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

Year: 2022

Type: article

Source: Advanced Energy Materials

DOI: 10.1002/aenm.202200553

Authors: Austin D. Sendek, Brandi Ransom, Ekin D. Cubuk, Lenson A. Pellouchoud, Jagjit Nanda, Evan J. Reed

Institutions: Stanford University, Google (United States), Oak Ridge National Laboratory

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: closed

Funders: National Science Foundation, U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Air Force Office of Scientific Research

Awards: DMREF: Collaborative research: Data driven discovery of synthesis pathways and distinguishing electronic phenomena of 1D van der Waals bonded solids, Graduate Research Fellowship Program (GRFP), G5987489617

OpenAlex ID: W4283828978

FWCI: 5.706

Cites: 80

Cited by: 100

Related to: 10