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Deep learning for automated classification and characterization of amorphous materials

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Abstract

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 a procedure to identify important structural features in materials that could be missed by standard techniques and give unique insight into how these neural networks process data.

Graphical abstract: Deep learning for automated classification and characterization of amorphous materials

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Publication details

The article was received on 20 Sep 2019, accepted on 26 Nov 2019 and first published on 27 Nov 2019


Article type: Paper
DOI: 10.1039/C9SM01903K
Soft Matter, 2019, Advance Article

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    Deep learning for automated classification and characterization of amorphous materials

    K. Swanson, S. Trivedi, J. Lequieu, K. Swanson and R. Kondor, Soft Matter, 2019, Advance Article , DOI: 10.1039/C9SM01903K

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