Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals
Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification ofstochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations ofmetal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyseswhere thousands of random realizations are necessary.