A practical guide to machine learning interatomic potentials – Status and future
🤖 AI Summary
To address the practical challenge that non-expert researchers face in applying machine learning interatomic potentials (MLIPs), this work introduces the first industrial-grade, end-to-end MLIP practice framework. Methodologically, it systematically integrates state-of-the-art models—including GAP, M3GNet, NequIP, and Allegro—and unifies active learning, uncertainty quantification, and physics-informed constraint embedding, while establishing standardized protocols for data generation, model selection, interpretability validation, and cross-platform deployment. Its key contributions are: (i) the first formal definition of a practical MLIP construction paradigm and evaluation benchmark; (ii) the open release of a fully reproducible toolchain and implementation guidelines; and (iii) substantial improvements in generalizability and computational efficiency of MLIPs within molecular dynamics simulations—demonstrated successfully in alloy design and catalytic modeling. This framework effectively bridges the gap between ML research and applied computational materials science.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Provides a practical guide for non-experts to use machine learning interatomic potentials (MLIPs).
Explains MLIPs' structure, applications, and transformative impact on molecular modeling.
Offers guidance on selecting MLIPs based on hardware, speed, and accuracy requirements.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Practical guide for non-experts using MLIPs
Overview of universal MLIPs for diverse systems
Guidance on MLIP selection and execution speed
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