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

本研究项目旨在通过使用原子尺度模拟数据训练机器学习模型来实现介观尺度模拟中的位错成核。自7个月前项目开始以来，通过训练机器学习模型(a)系统的形状，(b)应变，(c)全局势能，（d)应变间隔发生最后塑性事件的时间，(e)系统的大小），该人员能够训练一个能够以非常高的精度预测原子模拟中位错成核的模型。这一结果表明，确实有可能使用机器学习来预测位错的成核，并允许在介观尺度上开始实现模型。

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.