High-Throughput Computational Discovery and Intelligent Design of Two-Dimensional Functional Materials for Various Applications

Novel technologies and new materials are in high demand for future various applications to overcome the fundamental limitations of current techniques. For example, the nanomaterials are in high demand for the miniaturization of electric devices. Spintronics is one of the most viable solutions for green electric devices. The single-atom catalysts can provide a 100% utilization rate. Heterojunctions hold promise for applications in energy conversion and storage. Two-dimensional (2D) materials show promising applications in various applications because they can be tailored to the specific property on which a technology is based and may be compatible with other technologies. Although the number of experimentally discovered 2D materials is growing, the speed is very slow and only a few dozen 2D materials have been synthesized or exfoliated since the discovery of graphene. Recently, a novel computational technique, dubbed high-throughput computational materials design, has become a burgeoning area of materials science, which is a combination of quantum mechanical theory, materials information, and database construction with intelligent data mining. This new and powerful tool can greatly accelerate the discovery, design, and application of 2D materials by creating a database containing a large number of 2D materials with calculated fundamental properties and then intelligently mining (via high-throughput automation or machine learning) the database in the search for 2D materials with the desired properties for particular applications, such as energy conversion/storage, catalysis, water purification, electronics, and optoelectronics. In this Account, we summarize our recent progress in the emerging area of 2D materials discovery, database construction, 2D functional materials design, and device development by quantum-mechanical modeling, high-throughput calculations, and machine learning inspired by the materials genome concept. We developed an open 2D materials database- 2D materials encyclopedia (2DMatPedia; http://www.2dmatpedia.org/), which includes a variety of structural, thermodynamic, mechanical, electronic, and magnetic properties of more than 6000 two-dimensional materials. Using high-throughput computational screening and machinelearning techniques, we identified exotic 2D materials and heterojunctions with desired properties for several applications, such as the electrocatalysis of hydrogen and nitrogen evolution reactions, photocatalysis for water splitting, high Curie temperature ferromagnetic materials, ferromagnetic(ferroelectric) tunnel junctions, piezo(ferro)electricity, and excitonic solar cells. Our open 2D materials database with high-throughput calculations and proper advanced models will greatly reduce the experimental effort in trial and error, narrow the scope for both experimental and theoretical explorations, and thus boost the fast and sustainable development in the area of 2D materials. Despite the significant progress and successful deployment of materials informatics, data-driven materials discovery, high-throughput calculations, and machine learning as a major game change in the area of 2D materials science and technology, future challenges remain in several aspects, which are summarized in the Outlook.

Automated summary

This article introduces the progress in the discovery, design, and application of 2D materials using the novel computational technique of high-throughput computational materials design. The authors have developed an open 2D materials database and used computational screening and machine learning techniques to identify materials with desired properties, thereby advancing the field of 2D materials.