15 papers
NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
Ting Liang, Ke Xu, Eric Lindgren +16
While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting the…
Dislocation-loop formation is a first-order phase transition
Xiaoya Chang, Arsalan Hashemi, Nima Ghafari Cherati +3
Dislocation loops are the elementary product of radiation damage in crystals, limiting reactor-component lifetimes, power-electronics reliability and the coherence of solid-state q…
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…
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…
Optimal quantum reservoir learning in proximity to universality
Moein N. Ivaki, Matias Karjula, Tapio Ala-Nissila
The study of the boundary between classically simulable and computationally complex quantum dynamics is fundamental to understanding which physical resources may enable enhanced in…
Thermodynamics of Coherence-Selective Quantum Reset Protocols
Jishad Kumar, Achilleas Lazarides, Tapio Ala-Nissila
We develop an exact theory of coherence-selective stroboscopic resetting for quadratic open quantum systems within the single-particle density-matrix formalism. We focus on the sur…