遗传算法引导的晶界图深度学习：应对五个自由度的挑战
Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams: Addressing the Challenge of Five Degrees of Freedom
摘要：
晶界（GB）通常控制多晶材料的加工和性能。在这里，一个潜在的变革性的研究是通过构建GB属性图作为温度和体积组成的函数，也称为“肤色图”，作为一个通用的材料科学工具，与相图。然而，GB具有五个宏观（晶体学）自由度（DOF）。无论是通过实验还是建模，构造作为五个自由度函数的GBs属性图本质上都是一项“不可能完成的任务”。在这里，我们将等压半巨正则系综混合蒙特卡罗和分子动力学（混合MC/MD）模拟与遗传算法（GA）和深度神经网络（DNN）模型相结合，以应对这一重大挑战。DNN预测比原子模拟快约108，从而能够构建数百万个具有五个自由度的明显不同的GB的属性图。值得注意的是，优秀的预测精度已经实现了不仅是非对称倾斜和扭曲的GB，但也不对称倾斜和混合倾斜扭曲的GB;后者更复杂，更少了解，但他们是无处不在的，往往限制了真正的多晶体的性能作为薄弱环节。作为温度、本体组成和五个晶体学DOF（即，在一个7 D空间）打开了一个新的范例。
英文摘要：
Grain boundaries (GBs) often control the processing and properties of polycrystalline materials. Here, a potentially transformative research is represented by constructing GB property diagrams as functions of temperature and bulk composition, also called "complexion diagrams," as a general materials science tool on par with phase diagrams. However, a GB has five macroscopic (crystallographic) degrees of freedom (DOFs). It is essentially a "mission impossible" to construct property diagrams for GBs as a function of five DOFs by either experiments or modeling. Herein, we combine isobaric semi-grand-canonical ensemble hybrid Monte Carlo and molecular dynamics (hybrid MC/MD) simulations with a genetic algorithm (GA) and deep neural network (DNN) models to tackle this grand challenge. The DNN prediction is ~108 faster than atomistic simulations, thereby enabling the construction of the property diagrams for millions of distinctly different GBs of five DOFs. Notably, excellent prediction accuracies have been achieved for not only symmetric-tilt and twist GBs, but also asymmetric-tilt and mixed tilt-twist GBs; the latter are more complex and much less understood, but they are ubiquitous and often limit the performance properties of real polycrystals as the weak links. The data-driven prediction of GB properties as function of temperature, bulk composition, and five crystallographic DOFs (i.e., in a 7D space) opens a new paradigm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
