Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods challenging. Machine learning (ML) has proven versatile for accelerating or circumventing first-principl...

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Bibliographic Details
Main Authors: Haoyue Guo, Qian Wang, Annika Stuke, Alexander Urban, Nongnuch Artrith
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Energy Research
Subjects:
solid-state batteries
interfaces
atomistic simulations
first-principles calculations
machine learning
neural network potentials
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2021.695902/full

Internet

https://www.frontiersin.org/articles/10.3389/fenrg.2021.695902/full

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