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Optimizing cross-domain transfer for universal machine learning interatomic potentials.

Jaesun Kim1, Jinmu You1, Yutack Park1

  • 1Department of Materials Science and Engineering, Seoul National University, Seoul, Republic of Korea.

Nature Communications
|March 3, 2026
PubMed
Summary
This summary is machine-generated.

We developed a new training strategy for machine-learning interatomic potentials, enhancing model accuracy and transferability across diverse chemical domains. This approach accelerates materials discovery by enabling reliable predictions for molecules, crystals, and surfaces.

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Area of Science:

  • Computational materials science
  • Machine learning in chemistry
  • Quantum mechanics

Background:

  • Accurate and transferable machine-learning interatomic potentials (MLIPs) are crucial for accelerating materials and chemical discovery.
  • Existing universal MLIPs often suffer from overfitting to specific chemical spaces or computational methods, limiting their reliability across diverse applications.
  • This necessitates the development of robust models capable of generalizing across different chemical environments and functional domains.

Purpose of the Study:

  • To introduce a novel transferable multi-domain training strategy for developing accurate and generalizable MLIPs.
  • To enhance the out-of-distribution generalization capabilities of MLIPs while maintaining high in-domain accuracy.
  • To create a universal MLIP model, SevenNet-Omni, capable of bridging diverse chemical domains and quantum-mechanical fidelities.

Main Methods:

  • Implemented a multi-domain training strategy optimizing parameters via selective regularization.
  • Utilized a domain-bridging dataset to align potential-energy surfaces across different chemical environments.
  • Conducted systematic ablation experiments to validate the synergistic effects of the proposed strategies.
  • Trained the SevenNet-Omni model on 15 diverse open datasets encompassing molecules, crystals, and surfaces.

Main Results:

  • Demonstrated synergistic enhancement of out-of-distribution generalization and in-domain fidelity through the developed strategies.
  • Achieved state-of-the-art accuracy in cross-domain benchmarks, reaching chemical accuracy in various scenarios.
  • Successfully reproduced high-fidelity properties by transferring knowledge from larger, lower-accuracy databases.
  • SevenNet-Omni showed excellent performance in predicting adsorption energies for catalytic surfaces and metal-organic frameworks.

Conclusions:

  • The proposed transferable multi-domain training strategy significantly improves the reliability and applicability of MLIPs.
  • SevenNet-Omni represents a significant advancement toward universal, transferable interatomic potentials for materials and chemical discovery.
  • This framework provides a scalable pathway for developing models that bridge quantum-mechanical accuracy and broad chemical domain coverage.