機械学習によるマルチスケール物理シミュレーションの高度化

本研究项目旨在利用原子尺度的模拟数据训练机器学习模型，实现介观尺度模拟中位错的形核。自七个月前该项目开始以来，这位研究员通过训练一个机器学习模型来训练一个模型，该模型能够在原子模拟中以非常高的精度预测位错的形核：(A)系统的形状，(B)应变，(C)全局势能，(D)上一次发生塑性事件的应变间隔，以及(E)系统的大小。这一结果表明，使用机器学习来预测位错的形核确实是可能的，并允许在介观尺度上开始实施该模型。

This research project aims at implementing nucleation of dislocation in mesoscopic scale simulation by training a machine learning model with data obtained from simulations at the atomistic scale. Since the beginning of the project, seven months ago, the fellow was able to train a model able to predict with a very high degree of precision the nucleation of dislocation in atomistic simulations by training a machine learning model with : (a) the shape of the system, (b) the strain, (c) the global potential energy, (d) at which strain interval took place the last plastic event, and (e) the size of the system. This result shows that it is indeed possible to use machine learning to predict nucleation of dislocations and allows to start the implementation of the model at the mesoscopic scale.