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 …

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 …

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 …

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

The current predictive modeling techniques applied to Density Functional Theory (DFT)
computations have helped accelerate the process of materials discovery by providing …

Abstract Two-dimensional (2D) materials have been a hot research topic in the last decade,
due to novel fundamental physics in the reduced dimension and appealing applications …

Large language models (LLMs) such as generative pretrained transformers (GPTs) have
shown potential for various commercial applications, but their applicability for materials …

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 …