Machine learning atomic-scale stiffness in metallic glass

Due to lack of either translational or rotational symmetries at atomic-scale, predicting properties of amorphous materials from static structure is a challenging task. To circumvent the dilemma, a supervised machine-learning strategy via neural network is proposed to predict the atomic stiffness of metallic glass from discretized radial distribution function. The predicted stiffness and its spatial nature are calibrated with molecular dynamics...