Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

A reliable energy storage ecosystem is imperative for a renewable energy future, and
continued research is needed to develop promising rechargeable battery chemistries. To …

Strong optical absorption by a semiconductor is a highly desirable property for many
optoelectronic and photovoltaic applications. The optimal thickness of a semiconductor …

Developing algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …

Metal halide perovskites are the first solution processed semiconductors that can compete in
their functionality with conventional semiconductors, such as silicon. Over the past several …

This Methods/Protocols article is intended for materials scientists interested in performing
machine learning-centered research. We cover broad guidelines and best practices …

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

Materials Cloud is a platform designed to enable open and seamless sharing of resources
for computational science, driven by applications in materials modelling. It hosts (1) archival …

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to
focus on high-throughput computational screening (HTCS) methods to quickly assess the …

Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …