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With these results in hand, I next repeated the process but replacing every Sage parameter with the corresponding average parameter from Espaloma. This probably isn’t the best approach because many of the distributions look like the one shown below: there are multiple clusters of Espaloma-assigned values, and the Sage value is out in the middle. These may be good candidates for parameters that need to be split in Sage.
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The all parameter benchmark is still runningAs shown below, the results are more different from the esp-tors-10 results, as expected. And positively, esp-full appears to perform a bit better by all three metrics. This is without any re-fitting, so Espaloma’s average parameters for our SMIRKS patterns perform slightly better than our re-fit Sage 2.1.0 values.
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Possible Splits
The figure above shows the distribution of Espaloma parameters for b84. Whereas the Espaloma average is at 741.5, the Sage value is dragged down to 719.6 by a small cluster of pattern matches near 700. Zooming in on this cluster shows that nearly all of the carbons are bound to nitrogen, with 3/104 bound to oxygen instead. This suggests that this parameter could potentially be refined by adding a more specific parameter like [#7X3]-[#6X4:1]-[#1:2] or [#7X3,#8X2]-[#6X4:1]-[#1:2]. Examples of these molecules are shown in the figures below.
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