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

Posted by Caltech

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 […]


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