Work

AI‐Driven Decoding of Material Dynamics: From Machine Learning Potentials and Interpretability to Generative Prediction

Year: 2025

Type: article

Abstract: Understanding and predicting the dynamic processes that underpin material performance are crucial for designing next-generation materials capable of meeting the evolving demands of modern technologies. These processes-often occurring at atomic or molecular scales in condensed phases-remain notoriously difficult to probe experimentally. Artificial intelligence (AI) now offers a transformative framework that enables unprecedented realism in modeling, interpreting, and even generating multiscale dynamics under various external conditions. In this Review, we highlight recent advances in AI-based machine learning potentials, AI-guided interpretability, and generative AI for dynamic prediction, and demonstrate their applications to key challenges in materials science, including phase transitions in transforming materials and plastic deformation in metallic structural materials. Finally, we discuss the remaining challenges and outline future opportunities, aiming to inspire the development of AI-powered frameworks that can probe atomic-level dynamics and accelerate materials design.

Source: Advanced Materials

DOI: 10.1002/adma.202514626

Authors: Long Zhao, Hongxiang Zong

Institution: Xi'an Jiaotong University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation, Science Challenge Project

Awards: G3509882685, G3951563722, G4857673220, G6527309532, G8387334889