High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …

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 …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic
properties remains a formidable challenge for drug discovery. Deep learning provides us …

The overwhelming majority of scientific knowledge is published as text, which is difficult to
analyse by either traditional statistical analysis or modern machine learning methods. By …

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

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

The non-serendipitous discovery of materials with targeted properties is the ultimate goal of
materials research, but to date, materials design lacks the incorporation of all available …

Despite the rise in computing power, the large space of possible combinations of elements
and crystal structure types makes large-scale high-throughput surveys of stable materials …

The materials discovery problem usually aims to identify novel “outlier” materials with
extremely low or high property values outside of the scope of all known materials. It can be …