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 …

As data science and machine learning methods are taking on an increasingly important role
in the materials research community, there is a need for the development of machine …

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 …

Machine learning (ML) and artificial intelligence (AI) are quickly becoming commonplace in
materials research. In addition to the standard workflow of fitting a model to a large set of …

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

The rapidly growing interest in machine learning (ML) for materials discovery has resulted in
a large body of published work. However, only a small fraction of these publications includes …

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 …

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

(ML) and artificial intelligence (AI) to materials science are now commonplace. As materials
informatics has matured from a niche area of research into an established discipline, distinct …

Despite ML's impressive performance in commercial applications, several unique
challenges exist when applying ML in materials science applications. In such a context, the …