Rapid Prediction of Grain Boundary Network Evolution in Nanomaterials Utilizing a Generative Machine Learning Approach

作     者：Wang, Yuheng Kazemi, Amirreza Jing, Taotao Ding, Zhengming Li, Like Yang, Shengfeng 

作者机构：School of Mechanical Engineering Purdue University in Indianapolis IndianapolisIN46202 United States School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Department of Computer Science Tulane University New OrleansLA70118 United States Department of Mechanical and Aerospace Engineering University of Central Florida OrlandoFL32816 United States 

出 版 物：《SSRN》 

年 卷 期：2024年

核心收录：

主　　题：Forecasting 

摘      要：Predicting the behavior of nanomaterials under various conditions presents a significant challenge due to their complex microstructures. While high-fidelity modeling techniques, such as molecular dynamics (MD) simulations, are effective, they are also computationally demanding. Machine learning (ML) models have opened new avenues for the rapid exploration of design spaces. In this work, we developed a deep learning framework based on a conditional generative adversarial network (cGAN) to predict the evolution of grain boundary (GB) networks in nanocrystalline materials under mechanical loads, incorporating both morphological and atomic details. We conducted MD simulations on nanocrystalline tungsten and used the resulting ground-truth data to train our cGAN model. We assessed the performance of our cGAN model by comparing it to a Convolutional Autoencoder (ConvAE) model and examining the impact of changes in geometric morphology and loading conditions on the model s performance. Our cGAN model demonstrated high accuracy in predicting GB network evolution under a variety of conditions. This developed framework shows potential for predicting various materials behaviors across a wide range of nanomaterials. © 2024, The Authors. All rights reserved.