Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …

Graph neural networks (GNNs) have recently been used to learn the representations of
crystal structures through an end-to-end data-driven approach. However, a systematic top …

The joint automated repository for various integrated simulations (JARVIS) infrastructure at
the National Institute of Standards and Technology is a large-scale collection of curated …

Data-driven deep learning algorithms provide accurate prediction of high-level quantum-
chemical molecular properties. However, their inputs must be constrained to the same …

Contemporary materials science has seen an increasing application of various artificial
intelligence techniques in an attempt to accelerate the materials discovery process using …

Structural search and feature extraction are a central subject in modern materials design, the
efficiency of which is currently limited, but can be potentially boosted by machine learning …

Materials processing often occurs under extreme dynamic conditions leading to a multitude
of unique structural environments. These structural environments generally occur at high …

Quantifying the level of atomic disorder within materials is critical to understanding how
evolving local structural environments dictate performance and durability. Here, we leverage …

The development of deep learning interatomic potentials has enabled efficient and accurate
computations in quantum chemistry and materials science, circumventing computationally …