Hear from Tia Vieria as she describes what she likes about working at Citrine.
Find out about AI model accuracy and quantified uncertainty and the impact these have on business outcomes.
Find out why FAIR data standards are important in the corporate world as well as academia.
Our summer interns had productive summers. Read why they joined Citrine and what they did.
Using machine learning to create a new open database of corrosion-resistant alloys.
Design spaces define the search area for AI models. This blog explains what they are, and how they can be used in AI powered materials research.
Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling. This work […]
High-temperature alloy design is challenging, but crucial to aeronautical applications. Citrine is working on a project to improve the representation of alloys data and develop techniques to to accurately predict properties outside of the range of input data.
Extracting, cleaning and analyzing and visualizing MPEA data.
Over the past several years, the field of materials informatics has grown dramatically. Applications of machine learning (ML) and artificial intelligence (AI) to materials science are now commonplace. As materials informatics has matured from a niche area of research into an established discipline, distinct frontiers of this discipline have come into focus, and best practices […]