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 …

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to
battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily …

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

The optimal synthesis of advanced nanomaterials with numerous reaction parameters,
stages, and routes, poses one of the most complex challenges of modern colloidal science …

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to
focus on high-throughput computational screening (HTCS) methods to quickly assess the …

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting
opportunity to revolutionize inorganic materials discovery and development. Herein, we …

We provide a review of machine learning (ML) tools for material discovery and sophisticated
applications of different ML strategies. Although there have been a few published reviews on …

The rapid growth of materials chemistry data, driven by advancements in large-scale
radiation facilities as well as laboratory instruments, has outpaced conventional data …

Highlights•An overview of the elements of self-driving laboratories.•Recent advancements in
autonomous chemical science and engineering.•Recent progress in closed-loop …