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

Long Zhao1, Hongxiang Zong1

  • 1State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an, 710049, China.

Advanced Materials (Deerfield Beach, Fla.)
|December 6, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing materials science by enabling realistic modeling of atomic-level dynamics. This approach accelerates the design of advanced materials by predicting performance under various conditions.

Keywords:
atomistic simulationsinterpretabilitymachine learningmaterial dynamics

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Area of Science:

  • Materials Science
  • Computational Science
  • Artificial Intelligence

Background:

  • Predicting material performance requires understanding dynamic processes at atomic scales.
  • Experimental probing of these nanoscale dynamics is experimentally challenging.
  • Artificial intelligence (AI) offers advanced modeling capabilities for material dynamics.

Purpose of the Study:

  • To review recent advances in AI for modeling and predicting material dynamics.
  • To demonstrate AI applications in materials science challenges.
  • To outline future opportunities for AI in materials design.

Main Methods:

  • AI-based machine learning potentials for simulating dynamics.
  • AI-guided interpretability for understanding simulation results.
  • Generative AI for predicting dynamic behaviors.

Main Results:

  • AI enables realistic multiscale dynamic modeling.
  • AI applications shown in phase transitions and plastic deformation.
  • AI accelerates the prediction of material properties.

Conclusions:

  • AI provides a transformative framework for materials science.
  • AI-powered tools can probe atomic-level dynamics.
  • Future AI development will accelerate the design of next-generation materials.