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 …

The accuracy and efficiency of electronic-structure methods to understand, predict and
design the properties of materials has driven a new paradigm in research. Simulations can …

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

Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …

Computational material discovery is under intense study owing to its ability to explore the
vast space of chemical systems. Neural network potentials (NNPs) have been shown to be …

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class
of machine learning models remarkably well-suited for materials applications. To date, a …

We propose strategies that couple natural language processing with deep learning to
enhance machine capability for corrosion-resistant alloy design. First, accuracy of machine …

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

Artificial intelligence (AI) and machine learning (ML) have been increasingly used in
materials science to build predictive models and accelerate discovery. For selected …

Metamaterials are artificial materials designed to exhibit effective material parameters that
go beyond those found in nature. Composed of unit cells with rich designability that are …