Structure and Dynamics of Energy Materials from Machine Learning Simulations: A Topical Review†. Issue 11 (7th September 2021)

• Record Type: Journal Article
• Authors: Guan, Shu‐Hui; Shang, Cheng; Liu, Zhi‐Pan
• Is Part Of: Chinese journal of chemistry. Volume 39:Issue 11(2021)
• Page Start: 3144
• Page End: 3154
• Publication Date: 2021-09-07
• Subjects: Machine learning -- Materials science| Atomic simulation -- Thermodynamics -- Kinetics; Chemistry -- Periodicals; 540.5
• DOI: 10.1002/cjoc.202100299
• Languages: English
• ISSNs: 1001-604X

Abstract

Abstract: Energy materials featuring the capability to store and release chemical energy reversibly involve generally complex geometrical structures with multiple elements. It has been a great challenge to establish the quantitative relationship between the structure of materials and their dynamic physicochemical properties. In recent years, machine learning (ML) technique has demonstrated its great power in accelerating the research on energy materials. This topical review introduces the key ingredients and typical applications of ML to energy materials. We mainly focus on the ML based atomic simulation via ML potentials in different architectures/implementations, including high dimensional neural networks (HDNN), Gaussian approximation potential (GAP), moment tensor potentials (MTP) and stochastic surface walking global optimization with global neural network potential (SSW‐NN) method. Three cases studies, namely, Si, LiC and LiTiO systems, are presented to demonstrate the ability of ML simulation in assessing the thermodynamics and kinetics of complex material systems. We highlight that the SSW‐NN method provides an automated solution for global potential energy surface data collection, ML potential construction and ML simulation, which boosts the current ability for large‐scale atomic simulation and thus holds the great promise for fast property evaluation and material discovery. Abstract : Machine learning based atomic simulation via ML potentials in different architectures/implementations, including high dimensional neural networks (HDNN), Gaussian approximation potential (GAP), and stochastic surface walking global optimization with global neural network potential (SSW‐NN) method were overviewed. Three cases studies of energy materials are presented to demonstrate the ability of ML simulation in assessing the thermodynamics and kinetics of complex material systems.