Dec 2, 20242 min read
2024 Algorithm Improvements
Over the past year, we have reduced the data requirements 99% and the time by 97%
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Latest from PHIN
2026
PHIN selected by the U.S. DOE to compress catalyst development 15×
Through ARPA-E's CATALCHEM-E program, PHIN and PNNL are demonstrating a path from fifteen years of catalyst R&D to one.
Oct 30, 20243 min read
Scaling simulations to complex materials systems
Cropping allows us to generate training data from complex simulations to ensure the accuracy of 1000+ atom simulations
Sep 25, 20244 min read
Why is uncertainty quantification necessary in machine learning?
Understanding and analyzing a system's response to various inputs is central to any scientific or engineering R&D. Designing a new steel...
Aug 20, 20246 min read
Eliminating hallucinations in machine learning models
Digitizing materials development requires materials models that can predict materials behavior accurately, quickly and cheaply. Digital...