Machine learning accelerated carbon neutrality research using big data—from predictive models to interatomic potentials

Ling Jun Wu, Zhen Ming Xu, Zi Xuan Wang, Zijian Chen, Zhi Chao Huang, Chao Peng, Xiang Dong Pei, Xiang Guo Li, Jonathan P. Mailoa, Chang Yu Hsieh, Tao Wu, Xue Feng Yu, Hai Tao Zhao

Research output: Journal PublicationReview articlepeer-review

Abstract

Carbon neutrality has been proposed as a solution for the current severe energy and climate crisis caused by the overuse of fossil fuels, and machine learning (ML) has exhibited excellent performance in accelerating related research owing to its powerful capacity for big data processing. This review presents a detailed overview of ML accelerated carbon neutrality research with a focus on energy management, screening of novel energy materials, and ML interatomic potentials (MLIPs), with illustrations of two selected MLIP algorithms: moment tensor potential (MTP) and neural equivariant interatomic potential (NequIP). We conclude by outlining the important role of ML in accelerating the achievement of carbon neutrality from global-scale energy management, unprecedented screening of advanced energy materials in massive chemical space, to the revolution of atomic-scale simulations of MLIPs, which has the bright prospect of applications.

Original languageEnglish
JournalScience China Technological Sciences
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • big data
  • carbon neutrality
  • interatomic potentials
  • machine learning
  • molecular dynamics

ASJC Scopus subject areas

  • Materials Science (all)
  • Engineering (all)

Fingerprint

Dive into the research topics of 'Machine learning accelerated carbon neutrality research using big data—from predictive models to interatomic potentials'. Together they form a unique fingerprint.

Cite this