Abstract
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.
Highlights
Data-driven accelerated design of new materials is emerging as one of the most promising approaches for addressing the challenges in finding next-generation materials
Large scale generation of new materials with distinct structures and functions are highly desirable for widely used highthroughput screening based materials discovery
Faced with astronomically large structural design space, the generator models have to exploit the implicit sophisticated physicochemical and geometric rules and constraints embedded in the existing crystal materials
Summary
Data-driven accelerated design of new materials is emerging as one of the most promising approaches for addressing the challenges in finding next-generation materials. One of the main strategies for materials discovery is screening existing materials databases [1, 2, 3, 4]. Such approaches are severely limited by the scale and diversity of the existing structures in the repositories, such as ICSD and Materials Project (MP), which have about. Large-scale generation of stable hypothetical crystal structures is strongly needed to significantly expand the current materials repositories in both the quantity and compositional and structural diversity to increase the success rate of high-throughput screening of novel functional materials.
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