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 simulations. After which, the origin of atomic constraint is interpreted via the learning structural input. Inadequacy of the model is discussed in terms of incompleteness in both machine-learning configurational space and structural descriptor.

由于在原子尺度上缺乏平移或旋转对称性，从静态结构预测非晶材料的特性是一项具有挑战性的任务。为了避免这种困境，提出了一种通过神经网络的监督机器学习策略，以根据离散的径向分布函数预测金属玻璃的原子刚度。预测的刚度及其空间性质通过分子动力学模拟进行校准。之后，通过学习结构输入解释原子约束的起源。从机器学习配置空间和结构描述符的不完整性方面讨论了模型的不足之处。