Machine learning force field for Fe-H system and investigation on role of hydrogen on the crack propagation in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si15.svg"> <mml:mrow> <mml:mi mathvariant="bold-italic">α</mml:mi> </mml:mrow> </mml:math> -Fe
Buyu Zhang, Mark Asta, Lin‐Wang Wang
Abstract
A machine learning force field of Fe-H is constructed to study the hydrogen embrittlement via molecular dynamics (MD) simulations. The machine learning force field (MLFF) is obtained by learning the results of the density functional theory (DFT) MD calculation for various configurations, using a neural networks (NNs) model, with decomposed atomic energies in DFT calculations. Various tests are carried out to ensure that the MLFF describes well the statistic and dynamic property of iron + hydrogen systems. Using this MLFF, MD simulations of model iron sample with a small pre-crack tip at different grain boundaries are carried out with different loadings. We found that a high concentration of H ahead the crack tip will enhance the propagation of the crack, while the degree of enhancement depends on the type of the grain boundary. We also found the formation of microvoid ahead of crack and it might also contribute to the crack propagation.