Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

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 …

Although data-driven approaches have made significant strides in various scientific fields,
there has been a lack of systematic summaries and discussions on their application in 2D …

Calculations of point defect energetics with Density Functional Theory (DFT) can provide
valuable insight into several optoelectronic, thermodynamic, and kinetic properties. These …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

High-throughput density functional theory (DFT) calculations allow for a systematic search
for conventional superconductors. With the recent interest in two-dimensional (2D) …

Solar energy plays an important role in solving serious environmental problems and
meeting the high energy demand. However, the lack of suitable materials hinders further …

As an implementation tool of data intensive scientific research methods, machine learning
(ML) can effectively shorten the research and development (R&D) cycle of new materials by …