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

Haoyue Guo,Nongnuch Artrith,Qian Wang,Annika Stuke,Alexander Urban

doi:10.3389/fenrg.2021.695902

Haoyue Guo, Nongnuch Artrith + Show 3 more

Open Access

https://doi.org/10.3389/fenrg.2021.695902

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Abstract

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-principles calculations, thereby facilitating the modeling of materials properties that are otherwise hard to access. ML potentials trained on accurate first principles data enable computationally efficient linear-scaling atomistic simulations with an accuracy close to the reference method. ML-based property-prediction and inverse design techniques are powerful for the computational search for new materials. Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

Highlights

Li-ion batteries (LIBs) (Whittingham, 1976; Mizushima et al, 1980; Li et al, 2017b, Li et al, 2018a) have enabled a revolution in portable electronics, but the global transition to a clean energy economy based on renewable sources will require the development of a new generation of batteries that addresses the needs of grid-level storage and transportation
The results show that both cation (Co and P) mixing and anion (O and S) mixing are likely to occur in the interfacial region, and the migration of Li ions toward the anode results in the formation of a Li+-depleted layer, which is considered as the origin of the high interfacial resistance
We surveyed the current state of machine learning accelerated atomistic modeling of solid-state battery materials with a focus on applications of machine-learning potentials, property prediction models, and inverse design

Summary

Introduction

Li-ion batteries (LIBs) (Whittingham, 1976; Mizushima et al, 1980; Li et al, 2017b, Li et al, 2018a) have enabled a revolution in portable electronics, but the global transition to a clean energy economy based on renewable sources will require the development of a new generation of batteries that addresses the needs of grid-level storage and transportation To this end, computational materials discovery has become an important companion to conventional experimentation. Machine Learning for Solid-State Batteries already led to the discovery of novel battery materials (Kirklin et al, 2013; Er et al, 2015; Jain et al, 2016) Firstprinciples methods, such as electronic density-functional theory (DFT) (Hohenberg and Kohn, 1964; Kohn and Sham, 1965; Burke, 2012), are computationally demanding, and simulations are currently limited to small, typically crystalline structure models with less than 1,000 atoms and less than nanosecond time scales. It is challenging to investigate non-ideal atomic structures with first-principles, such as the defected or amorphous phases and interphases that are formed at the electrode|electrolyte interfaces in LIBs (Yu and Manthiram, 2018)

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