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High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
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High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
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High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
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Journal Article
High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
2021
Overview
High‐throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of sufficient and diverse materials in current materials repositories such as the open quantum materials database (OQMD). Recent progress in deep learning have enabled generative strategies that learn implicit chemical rules for creating hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generative design of novel cubic materials. When trained on 375 749 ternary materials from the OQMD database, the authors show that the model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such materials have been verified by phonon dispersion calculation. Considering the importance of cubic materials in wide applications such as solar panels, the GAN model provides a promising approach to significantly expand existing materials repositories, enabling the discovery of new functional materials via screening. The new crystal structures discovered are freely accessible at www.carolinamatdb.org. A deep neural network based generative model for high‐throughput generation of cubic materials is proposed. By creating 20 million virtual materials and applying machine learning and DFT based fast screenings, their model has not only rediscovered most of known cubic materials, but generated large number of new cubic structures with new prototypes: 506 new‐prototype stable materials have been verified computationally.
Publisher
John Wiley & Sons, Inc,John Wiley and Sons Inc,Wiley
