Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

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

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

There is growing interest in applying machine learning techniques in the research of
materials science. However, although it is recognized that materials datasets are typically …

Accelerating materials research by integrating automation with artificial intelligence is
increasingly recognized as a grand scientific challenge to discover and develop materials …

Abstract Machine learning (ML) models utilizing structure-based features provide an efficient
means for accurate property predictions across diverse chemical spaces. However …

The current interest in big data, machine learning and data analytics has generated the
widespread impression that such methods are capable of solving most problems without the …

We provide a brief discussion of “What is machine learning?” and then give a number of
examples of how these methods have recently aided the design and discovery of new …

Perovskite solar cells, with efficiencies of 22.1%, are the only solution-processable
technology to outperform multicrystalline silicon and thin-film solar cells. Whereas …

The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error”
discovery approaches. As an alternative route, the Materials Genome Initiative has largely …