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cond-mat.mtrl-sci2026
Orchestr.AI: A Unified Workflow for Training and Benchmarking of Machine-Learned Interatomic Potentials
Zain Ul Abideen, Mario Fernández-Pendás, Muhammad Usman +4
Machine-learning force fields enable atomistic simulations at near-reference accuracy and substantially reduced computational cost, but their development remains fragmented across…
cond-mat.mtrl-sci2026
Benchmarking Machine-Learning Interatomic Potentials for Dynamical Stability in Inorganic Semiconductor Nanocrystals: A CdSe Case Study
M. Usman, M. Suleymanova, Z. U. Abideen +2
Machine-learning interatomic potentials (MLIPs) enable nanosecond-scale atomistic simulations of inorganic semiconductor nanocrystals, but low errors on held-out configurations do…