Kavli Affiliate: Wei Gao
| First 5 Authors: Fei Shuang, Zixiong Wei, Kai Liu, Wei Gao, Poulumi Dey
| Summary:
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/{AA} 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.
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