High‐throughput computational screening of layered and two‐dimensional materials

Abstract

The successful exfoliation of graphene and other kinds of two‐dimensional (2D) materials from their corresponding three‐dimensional (3D) bulk counterparts has inspired researchers to screen layered bulk compounds as parent materials for potential 2D materials. With the rapid development of supercomputers and high‐performance computations, high‐throughput materials screening is a growing new power in materials science for the discovery of novel kinds of materials with desired functionality. Recently, many parent 3D bulks have been identified by high‐throughput screening from materials databases for potential 2D materials, and several 2D materials databases are established through numerous efforts. In addition, on the basis of high‐throughput computations for electronic properties and data‐mining algorithms, several functional layered and 2D materials, such as electrode materials, photohydrolytic catalysts, half metals, piezoelectric monolayers and heterostructures, have been achieved. In this review, we summarize the recent progress in the high‐throughput screening of parent candidates for 2D materials and their further applications, and the challenges and perspectives are also briefly discussed. We highly expect that this review could lead the way forward in the discovery of novel 2D materials and provide a guide for the further development of 2D materials. This article is categorized under: Structure and Mechanism > Computational Materials Science Electronic Structure Theory > Density Functional Theory

An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery

“…An exciting arena of materials design is the development of new functional materials for energy conversion and storage. Multi-scale high-throughput computational screening studies have recently been utilized as systematic approaches 31,32 in order to accelerate the discovery of new 2D energy materials for photovoltaics [33][34][35] as well as photocatalytic solar fuel generation through the conversion of feedstock molecules, including H 2 O 12,31,[36][37][38] , CO 2 37,39,40 , and N 2 37 . To illustrate the use of AI methods for the virtual screening of candidate 2D materials, Fig.…”

Machine learned features from density of states for accurate adsorption energy prediction

“…Following the development of robust quantum chemistry software and the availability of high-performance computing, high-throughput screening is now an increasingly widespread approach for materials discovery 1 – 7 . The field of heterogeneous catalysis is no exception to this trend 8 – 11 .…”

High‐throughput computational materials screening and discovery of optoelectronic semiconductors

“…There emerge several recent studies in HT computational calculation and screening of 2D layered materials. 46,[180][181][182][183][184][185][186] Based on HT computational screening strategies, Björkman et al 181 screened the vdW-bonded layered structures via geometric criteria by searching the ICSD database. Lebègue et al 182 screened novel 2D materials based on the pure structure geometries from the ICSD database.…”

“…A recent review also summarized HT computational screening of layered and 2D electrode materials, half metals, piezoelectric monolayers, and heterostructures. 186 By combining ML techniques and HT DFT calculations, Lu et al 187 screened the hybrid organic-inorganic perovskites for PV applications. They successfully identified six orthorhombic lead-free hybrid organic-inorganic perovskites with ideal bandgap (0.9-1.6 eV) and room temperature thermal stability from 5,158 unexplored hybrid organicinorganic perovskites.…”