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physics.comp-ph2026
Machine-learned prediction of carbon interstitial clusters in diamond
Xiaoya Chang, Arsalan Hashemi, Nima Ghafari Cherati +3
Diamond hosts optically active point defects central to quantum technologies, yet the carbon self-interstitials introduced during growth and irradiation compete with them and form…
physics.comp-ph2026
TorchNEP: Ultra-Efficient and Accurate Training of Neuroevolution Potentials
Yong-Chao Wu, Xiaoya Chang, Tero Mäkinen +5
Neuroevolution Potential (NEP) is one of the most efficient machine-learned interatomic potential frameworks for large-scale atomistic simulations. However, its original training s…