Deep learning for automated classification and characterization of amorphous materials

作     者：Swanson, Kirk Trivedi, Shubhendu Lequieu, Joshua Swanson, Kyle Kondor, Risi 

作者机构：Pritzker School for Molecular Engineering University of Chicago ChicagoIL60637 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States Institute for Computational and Experimental Research in Mathematics Brown University ProvidenceRI02903 United States Materials Research Laboratory University of California Santa BarbaraCA93106 United States Department of Statistics University of Chicago ChicagoIL60637 United States Center for Computational Mathematics Flatiron Institute New YorkNY10010 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2019年

核心收录：

主　　题：Neural networks 

摘      要：It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide us with a procedure to identify important structural features in materials that could be missed by standard techniques and give us a unique insight into how these neural networks process data. Copyright © 2019, The Authors. All rights reserved.