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Emil Annevelink
Dec 22 min read
2024 Algorithm Improvements
Over the past year, we have reduced the data requirements 99% and the time by 97%
Emil Annevelink
Oct 303 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
Emil Annevelink
Sep 254 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...
Emil Annevelink
Aug 206 min read
Eliminating hallucinations in machine learning models
Digitizing materials development requires materials models that can predict materials behavior accurately, quickly and cheaply. Digital...
Emil Annevelink
Jun 206 min read
How are we bringing machine learning to market? Introducing PHIN-atomic
Digitizing materials development requires the accurate prediction of materials properties at lower costs than an equivalent experiment . . .
Emil Annevelink
Jun 65 min read
Why we started Physics Inverted Materials
The rise of AI has decreased the time it takes to develop material models. However, machine learning models introduce a new issue . . .
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