机器学习型分子力场在金属材料相变与变形领域的研究进展
Machine-Learning Force Fields for Metallic Materials: Phase Transformations and Deformations
机器学习型分子力场在金属材料相变与变形领域的研究进展 |
| 李志尚, 赵龙, 宗洪祥, 丁向东 |
|
Machine-Learning Force Fields for Metallic Materials: Phase Transformations and Deformations |
| LI Zhishang, ZHAO Long, ZONG Hongxiang, DING Xiangdong |
| 图2 DeepMD模型的示意图：下方的框架是其中深度神经网络的放大图；原子i所有相邻原子之间的距离矩阵{ Rij }，即环境矩阵，首先转换为描述矩阵{ Dij }，再传入到隐藏层计算原子能量Ei[ |
| Fig.2 Schematic of the DeepMD model. The frame in the box is an enlargement of a deep neural network (DNN). The relative positions of all neighbors with respect to atom i, i.e., { Rij }, is first converted to descriptor matrix { Dij }, then passed to the hidden layers to compute Ei[ |
|
|