2 papers
cond-mat.mtrl-sci2026
Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Yonatan Kurniawan, Mingjian Wen, Ellad B. Tadmor +1
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful t…
cond-mat.mtrl-sci2026
Inverse design of bespoke interatomic potentials via active learning by information-matching
Yonatan Kurniawan, Logan D. Williams, Amit Samanta +6
Interatomic potentials (IPs) enable large-scale atomistic simulations beyond the reach of first-principles methods, but their predictive reliability depends critically on the selec…