Open AccessModelling and understanding battery materials with machine-learning-driven atomistic simulations
Author(s) -
Volker L. Deringer
Publication year - 2020
Publication title -
jphys energy
Language(s) - English
Resource type - Journals
ISSN - 2515-7655
DOI - 10.1088/2515-7655/abb011
Subject(s) - battery (electricity) , quantum , anode , computer science , nanometre , scale (ratio) , atomic units , materials science , electrolyte , nanotechnology , amorphous solid , interface (matter) , lithium ion battery , power (physics) , electrode , physics , chemistry , organic chemistry , bubble , quantum mechanics , maximum bubble pressure method , parallel computing , composite material
The realistic computer modelling of battery materials is an important research goal, with open questions ranging from atomic-scale structure and dynamics to macroscopic phenomena. Quantum-mechanical methods offer high accuracy and predictive power in small-scale atomistic simulations, but they quickly reach their limits when complex electrochemical systems are to be studied—for example, when structural disorder or even fully amorphous phases are present, or when reactions take place at the interface between electrodes and electrolytes. In this Perspective, it is argued that emerging machine learning based interatomic potentials are promising tools for studying battery materials on the atomistic and nanometre length scales, affording quantum-mechanical accuracy yet being many orders of magnitude faster, and thereby extending the capabilities of current battery modelling methodology. Initial applications to solid-state electrolyte and anode materials in lithium-ion batteries are highlighted, and future directions and possible synergies with experiments are discussed.