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A foundation model for atomistic materials chemistry.

Ilyes Batatia1, Philipp Benner2, Yuan Chiang3,4

  • 1Engineering Laboratory, University of Cambridge, Trumpington St. and JJ Thomson Ave., Cambridge, United Kingdom.

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|November 13, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

A new general-purpose machine-learning (ML) force field, MACE-MP-0, enables stable atomistic simulations for diverse materials. This foundation model democratizes advanced modeling by offering broad applicability and ease of use for researchers.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Physics

Background:

  • Atomistic simulations using first-principles (ab initio) methods are crucial for understanding chemistry and materials.
  • Machine-learned (ML) force fields have advanced atomistic modeling, enabling simulations at unprecedented scales.
  • Existing ML force fields often require extensive system-specific development and lack transferability.

Purpose of the Study:

  • To develop a general-purpose, transferable atomistic machine-learning model.
  • To demonstrate the model's capability for stable molecular dynamics across a wide range of systems.
  • To lower the barrier to entry for advanced atomistic simulations.

Main Methods:

  • Training a general-purpose ML model (MACE-MP-0) on a public dataset.
  • Validating the model's performance on diverse physical science problems.
  • Assessing the model's accuracy and transferability for various materials and molecules.

    • Main Results:

    • The MACE-MP-0 model enables stable molecular dynamics for a wide array of molecules and materials.
    • Demonstrated qualitative and quantitative accuracy across solids, liquids, gases, reactions, interfaces, and protein dynamics.
    • The model functions as a "foundation" model, applicable out-of-the-box or fine-tuned for specific applications.

    Conclusions:

    • A single, general-purpose ML force field can achieve high accuracy across diverse atomistic systems.
    • This approach significantly accelerates reliable simulations for experienced users and lowers the entry barrier for beginners.
    • Foundation models represent a significant step toward democratizing ML-driven atomistic modeling.