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While OpenFF has yet to move to a full neural network force field in the framework of Espaloma, it may be useful to use Espaloma as a reference, and we may be able to use Espaloma to determine areas where OpenFF parameters need improvement. For example, there may be cases where OpenFF uses one parameter to encode a particular chemistry, that Espaloma splits into many different values. Here, Trevor Gokey’s work on automated parameter generation could come in handy for partitioning espaloma data. If assigned parameter values are significantly different between Espaloma and OpenFF, that would also be worth exploring.
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Experiments
Big Torsion Deviations
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As 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
b84
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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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b5
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For b5, the long tail of values below the Sage value is due to C-C bonds between two fused aromatic rings, as shown in the figure below. It would probably be difficult to split b5 enough times to replicate all of the different Espaloma values along this tail, but it might be reasonable to split it at least once to separate the main body of Espaloma values centered at 843 from the tail.
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b1
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Initially, I thought these were mostly CX4-CX4 bonds in rings. Many of them are actually in three- or four-member rings, but there are some more typical C-C bonds too. There are actually 396 of these, which was a bit too much to crawl through by hand. A random selection of 25 are shown below. As a result of the variety, I’m not sure this one is a great candidate for splitting. Maybe we’re looking for distributions with two (or more) distinct peaks rather than ones with long tails like b1 and b5.
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b2
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As shown in the figure below, all of these outliers are due to C-C bonds where one or both C is attached to an O, usually a carbonyl or carboxyl group.
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b7
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b16
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b4
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b21
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b6
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b10
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b9
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b13
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b59
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b3
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b19
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b11
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b52
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b88
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b17
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b14
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b20
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b36
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