Yonatan Kurniawan

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials

Abstract Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful tools for large-scale materials modeling. The accuracy of MLIPs is typically validated on a...