Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Entropy-related phase stabilization can allow compositionally complex solid solutions of
multiple principal elements. The massive mixing approach was originally introduced for …

Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms,
is becoming an important tool in the fields of materials and mechanical engineering …

Abstract Machine learning models are increasingly used in materials studies because of
their exceptional accuracy. However, the most accurate machine learning models are …

Materials-by-design is a paradigm to develop previously unknown high-performance
materials. However, finding materials with superior properties is often computationally or …

The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …

Mechanical metamaterials are meticulously designed structures with exceptional
mechanical properties determined by their microstructures and constituent materials …

In recent years, machine learning (ML) techniques are seen to be promising tools to
discover and design novel materials. However, the lack of robust inverse design approaches …

This is a viewpoint paper on recent progress in the understanding of the microstructure–
property relations of advanced high-strength steels (AHSS). These alloys constitute a class …

Machine learning (ML) has been perceived as a promising tool for the design and discovery
of novel materials for a broad range of applications. In this prospective paper, we summarize …