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 …

The development of new batteries has historically been achieved through discovery and
development cycles based on the intuition of the researcher, followed by experimental trial …

Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …

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

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

Applied machine learning has rapidly spread throughout the physical sciences. In fact,
machine learning-based data analysis and experimental decision-making have become …

Autonomous synthesis and characterization of inorganic materials require the automatic and
accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep …

Determining the crystal structure is a critical step in the discovery of new functional materials.
This process is time consuming and requires extensive human expertise in crystallography …

Abstract Machine learning (ML) has become a valuable tool to assist and improve materials
characterization, enabling automated interpretation of experimental results with techniques …

Efficient synthesis recipes are needed to streamline the manufacturing of complex materials
and to accelerate the realization of theoretically predicted materials. Often, the solid-state …