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

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.

Automated summary

This review provides a detailed overview of the role of machine learning in accelerating carbon neutrality research, specifically in energy management, screening of novel energy materials, and ML interatomic potentials. It highlights the applications of two selected MLIP algorithms and emphasizes the important role of machine learning in advancing global-scale energy management, unprecedented screening of advanced energy materials in massive chemical space, and revolutionizing atomic-scale simulations of MLIPs.