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 …

Inspired by the gradient structures of biological materials, researchers have explored
compositional and structural gradients for about 40 years as an approach to enhance the …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

The screening of novel materials with good performance and the modelling of quantitative
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …

Abstract The Open Quantum Materials Database (OQMD) is a high-throughput database
currently consisting of nearly 300,000 density functional theory (DFT) total energy …

Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …

Data‐driven science is heralded as a new paradigm in materials science. In this field, data is
the new resource, and knowledge is extracted from materials datasets that are too big or …

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 …

A very active area of materials research is to devise methods that use machine learning to
automatically extract predictive models from existing materials data. While prior examples …

Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …