论文标题
AB初始准确性的MOS2中的深度学习中的原子间潜力
Deep-learning interatomic potential for irradiation damage simulations in MoS2 with ab initial accuracy
论文作者
论文摘要
高度需要准确地描述辐射损伤过程的电位，以找出在辐射环境下各种新发现材料的原子级响应。在这项工作中，我们通过组合全电子计算，一种主动学习抽样方法和混合学习模型来引入单层MOS2的深度学习间潜力。这种潜力不仅可以在近平衡材料特性的预测上具有良好的性能，包括晶格常数，弹性系数，能量应力曲线，声子光谱，缺陷形成能和位移阈值，还可以重现AB初始辐照损伤过程，具有高质量。进一步的辐照模拟表明，一个单一的高能离子可以产生一个大纳米孔，直径超过2 nm，或一系列多个纳米孔，随后的500 keV Au+离子辐照实验对此进行了定性验证。这项工作提供了一种有希望且可行的方法，以模拟以前所未有的准确性来模拟巨大新发现材料的辐照效应。
Potentials that could accurately describe the irradiation damage processes are highly desired to figure out the atomic-level response of various newly-discovered materials under irradiation environments. In this work, we introduce a deep-learning interatomic potential for monolayer MoS2 by combining all-electron calculations, an active-learning sampling method and a hybrid deep-learning model. This potential could not only give an overall good performance on the predictions of near-equilibrium material properties including lattice constants, elastic coefficients, energy stress curves, phonon spectra, defect formation energy and displacement threshold, but also reproduce the ab initial irradiation damage processes with high quality. Further irradiation simulations indicate that one single highenergy ion could generate a large nanopore with a diameter of more than 2 nm, or a series of multiple nanopores, which is qualitatively verified by the subsequent 500 keV Au+ ion irradiation experiments. This work provides a promising and feasible approach to simulate irradiation effects in enormous newly-discovered materials with unprecedented accuracy.
