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 first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

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

We consider representation learning on periodic graphs encoding crystal materials. Different
from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a …

Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …

DScribe is a software package for machine learning that provides popular feature
transformations (“descriptors”) for atomistic materials simulations. DScribe accelerates the …

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 networks are a new machine learning (ML) paradigm that supports both relational
reasoning and combinatorial generalization. Here, we develop universal MatErials Graph …

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 …

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