Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

The screening of advanced materials coupled with the modeling of their quantitative
structural-activity relationships has recently become one of the hot and trending topics in …

Graph neural networks (GNN) have been shown to provide substantial performance
improvements for atomistic material representation and modeling compared with descriptor …

Abstract The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an
integrated infrastructure to accelerate materials discovery and design using density …

Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

We describe a first open-access database of experimentally investigated hybrid organic–
inorganic materials with a two-dimensional (2D) perovskite-like crystal structure. The …

The power of quantum chemistry to predict the ground and excited state properties of
complex chemical systems has driven the development of computational quantum chemistry …

The astonishing progress achieved in perovskite solar cells in recent years has coincided
with the growing interest in machine learning (ML) for material discovery, and the number of …

Halide perovskite materials serve as excellent candidates for solar cell and optoelectronic
devices. Recently, the design of the halide perovskite materials is greatly facilitated by …

Discovery of high-performance materials remains one of the most active areas in
photovoltaics (PV) research. Indirect band gap materials form the largest part of the …