Advances in modeling complex materials: The rise of neuroevolution potentials

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Interatomic potentials are essential for driving molecular dynamics (MD) simulations, directly impacting the reliability of predictions regarding physical and chemical properties materials. In recent years, machine-learned (MLPs), trained against first-principles calculations, have become a new paradigm in materials modeling as they provide desirable balance between accuracy computational cost. The neuroevolution potential (NEP) approach, implemented open-source GPUMD software, has emerged promising potential, exhibiting impressive exceptional efficiency. This review provides comprehensive discussion on methodological practical aspects NEP along with detailed comparison other representative state-of-the-art MLP approaches terms training accuracy, property prediction, We also demonstrate application approach to perform accurate efficient MD addressing complex challenges that traditional force fields typically cannot tackle. Key examples include structural liquid amorphous materials, order alloy systems, phase transitions, surface reconstruction, material growth, primary radiation damage, fracture two-dimensional nanoscale tribology, mechanical behavior compositionally alloys under various loadings. concludes summary perspectives future extensions further advance this rapidly evolving field.

Language: Английский

Consistency between the Green-Kubo formula and the Lorentz model for predicting the infrared dielectric function of polar materials

Physical review. B./Physical review. B, Journal Year: 2025, Volume and Issue: 111(18)

Published: May 9, 2025

Language: Английский