Content Library

Single-crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure determination and refinement is not always...

Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the...

The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes...

Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications such as printed electronics, organic solar cells, and...

This data article presents a compilation of mechanical properties of 630 multi-principal element alloys (MPEAs). Built upon recently published MPEA databases, this article includes updated records from previous reviews (with...

New electrochemical reactions are needed to reduce the carbon footprint of chemical production. Use AI to find new catalysts.

How an AI-empowered workflow enables the agility needed to respond to supply chain issues.

In this webinar, participants will learn how the Citrine Platform combines AI and smart data management to accelerate research and development and improve customer responsiveness.

Summary of peer reviewed, papers showing experimentally verified predictions of material properties.

Materials and Chemicals data is sparse. It is not “Big Data”. Which is why you need to use every bit of domain knowledge expert domain knowledge at your disposal...