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What is our notion of best-fit for generation, prediction, and relaxation? #12
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Another option that struck me is using a time-split. For example:
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Also can take a look at the model accuracy for Matbench task(s) as a way to probe the "quality" of the |
DFT simulations will also be important as a high-cost validation. |
From mp-time-split:
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Having trouble getting CDVAE to run txie-93/cdvae#19, but can probably splice out the |
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Might hold off on CDVAE metrics for now. See txie-93/cdvae#10 |
As an update, |
EDIT: see also issues with the "notion of best" label
Relaxation is probably the most straightforward - use some crystal distance. Prediction can be about checking against known allotropes, where we take the lowest crystal distance among the allotropes. Generation is the least straightforward. Perhaps a Pareto hypervolume metric via a fictitious adaptive design campaign (e.g. bulk modulus vs. energy above hull)? Perform hyperparameter optimization and then do DFT as the final validation.
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