In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.

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The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

Today the syntax of many languages is defined by using context-free grammars. These syntax definitions suffer from a major drawback: grammars do not allow the definition of abstract, reusable concept definitions. Especially in families of related languages, where multiple languages often share the same concepts, this limitation leads to unnecessary reproduction of concept definitions and a missing shared base for these related languages. Metamodels can contain inheritance hierarchies of concepts; thus multiple specifications can reuse and refine existing shared concept definitions. Therefore we propose a method to develop metamodels from existing syntax definitions. We explain our method by applying it to SDL-2000. The method starts with a mapping from BNF grammars into simple preliminary metamodels. Then, by supplying a relation between elements of these simple metamodels and abstract concepts, these metamodels are automatically transformed into metamodels that use existing descriptions of abstract concepts and thus allow a shared basis of common abstract concepts definitions.