Harmonic, quasi-harmonic, and anharmonic phonon properties of crystals are getting to be
better predicted using first-principles phonon calculations by virtue of the progress of the …

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

The ever-growing availability of computing power and the sustained development of
advanced computational methods have contributed much to recent scientific progress …

In light of the pressing need for practical materials and molecular solutions to renewable
energy and health problems, to name just two examples, one wonders how to accelerate …

Transition metal oxides are promising candidates for the next generation of spintronic
devices due to their fascinating properties that can be effectively engineered by strain …

Machine Learning for Materials Science provides the fundamentals and useful insight into
where Machine Learning (ML) will have the greatest impact for the materials science …

With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …

The vastness of the materials design space makes it impractical to explore using traditional
brute-force methods, particularly in reticular chemistry. However, machine learning has …

In recent years, modeling and simulation of materials have become indispensable to
complement experiments in materials design. High‐throughput simulations increasingly aid …

The Open Databases Integration for Materials Design (OPTIMADE) application
programming interface (API) empowers users with holistic access to a growing federation of …