Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys

Abstract

Recent advances in machine learning, combined with the generation of extensive density functional theory (DFT) datasets, have enabled the development of universal machine learning interatomic potentials (uMLIPs). These models offer broad applicability across the periodic table, achieving first-principles accuracy at a fraction of the computational cost of traditional DFT calculations. In this study, we demonstrate that state-of-the-art pretrained uMLIPs can effectively replace DFT for accurately modeling complex defects in a wide range of metals and alloys. Our investigation spans diverse scenarios, including grain boundaries and general defects in pure metals, defects in high-entropy alloys, hydrogen-alloy interactions, and solute-defect interactions. Remarkably, the latest EquiformerV2 models achieve DFT-level accuracy on comprehensive defect datasets, with root mean square errors (RMSE) below 5 meV/atom for energies and 100 meV/Å for forces, outperforming specialized machine learning potentials such as moment tensor potential and atomic cluster expansion. We also present a systematic analysis of accuracy versus computational cost and explore uncertainty quantification for uMLIPs. A detailed case study of tungsten (W) demonstrates that data on pure W alone is insufficient for modeling complex defects in uMLIPs, underscoring the critical importance of advanced machine learning architectures and diverse datasets, which include over 100 million structures spanning all elements. These findings establish uMLIPs as a robust alternative to DFT and a transformative tool for accelerating the discovery and design of high-performance materials.

References in corpus (16)

• VASPKIT: A User-friendly Interface Facilitating High-throughput Computing and Analysis Using VASP Code
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• DeePMD-kit v2: A software package for Deep Potential models
• Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations
• General-purpose machine-learned potential for 16 elemental metals and their alloys
• DPA-2: a large atomic model as a multi-task learner
• Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
• Systematic Atomic Structure Datasets for Machine Learning Potentials: Application to Defects in Magnesium
• Tensor-reduced atomic density representations
• Universal correlation between electronic factors and solute-defect interactions in bcc refractory metals
• Modeling Extensive Defects in Metals through Classical Potential-Guided Sampling and Automated Configuration Reconstruction
• How Graph Neural Network Interatomic Potentials Extrapolate: Role of the Message-Passing Algorithm

Cited by in corpus (1)

• Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys