AI-Guided Closed-loop Synthesis of Nanoparticles for Catalysis
Learn how Citrine worked with SLAC National Accelerator laboratory to optimize synthesis parameters for nanoparticles using an AI-guided closed-loop system in just 12 hours.
Life At Citrine – Tia Vieira
Hear from Tia Vieria as she describes what she likes about working at Citrine.
Does model accuracy really matter?
Find out about AI model accuracy and quantified uncertainty and the impact these have on business outcomes.
Why Data FAIRness is important in the corporate world
Find out why FAIR data standards are important in the corporate world as well as academia.
Productive Summers At Citrine
Our summer interns had productive summers. Read why they joined Citrine and what they did.
A new open database of corrosion-resistant alloys
Using machine learning to create a new open database of corrosion-resistant alloys.
What is a Design Space?
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.
Utilization of machine learning to accelerate colloidal synthesis and discovery
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 […]
Addressing challenges in high-temperature alloy design
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.
Aggregating data to accelerate alloy design
Extracting, cleaning and analyzing and visualizing MPEA data.