Deep learning inter-atomic potential model for accurate irradiation damage simulations
Deep learning inter-atomic potential model for accurate irradiation damage simulations
复制标题
用于精确辐照损伤模拟的深度学习原子间势模型
DOI：
10.1063/1.5098061
复制
发表时间：
2019
影响因子：
4
通讯作者：
Jianming Xue
中科院分区：
文献类型：
--
作者：
Hao Wang;Xun Guo;Linfeng Zhang;Han Wang;Jianming Xue
我们提出了一种混合方案，该方案利用新开发的深度学习势能模型平滑地插值Ziegler-Biersack-Littmark（ZBL）筛选的核排斥势。由此产生的DP-ZBL模型不仅可以在近平衡材料性质的预测上提供整体良好的性能，而且还可以在原子彼此非常接近时捕获正确的物理学，这是辐射损伤事件的计算模拟中经常发生的事件。将该方法应用于面心立方铝体系的辐照损伤过程的模拟，发现该方法在缺陷形成能、碰撞级联演化、位移阈值能和残余点缺陷等方面都比ZBL改进的嵌入原子势及其变体有更好的描述.本文的工作为精确模拟辐照损伤过程提供了可靠可行的方案，为解决辐照效应领域中大量新发现材料缺乏精确潜力的困境开辟了新的机遇。
We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The resulting DP-ZBL model can not only provide overall good performance on the predictions of near-equilibrium material properties but also capture the right physics when atoms are extremely close to each other, an event that frequently happens in computational simulations of irradiation damage events. We applied this scheme to the simulation of the irradiation damage processes in the face-centered-cubic aluminium system, and found better descriptions in terms of the defect formation energy, evolution of collision cascades, displacement threshold energy, and residual point defects, than the widely-adopted ZBL modified embedded atom method potentials and its variants. Our work provides a reliable and feasible scheme to accurately simulate the irradiation damage processes and opens up new opportunities to solve the predicament of lacking accurate potentials for enormous newly-discovered materials in the irradiation effect field.