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Should we even do conformational sampling via MD | MD is about the worst possible way to sample conformers – CB CB: Freeform: SMILES => conformers with (crude) partition function Can get results in the form as minima Can use these as inputs to ff parametrization Chooses many conformers, does minimization in (implicit? LW forgets) solvent, normal mode analysis to get minima
DM: one thing I’ve done before is generate conformers, then start MD from each conformer, then cluster resulting conformers SB: porque no los dos? We can compare them. Makes the paper more interesting CB: MD can deviate from the minimum well, if we start from FreeForm conformers SB: QM optimizations should be lightly restrained, e.g. as is done for ELF. Loosening tolerances can make this cheaper
CB: Avoid thinking of it as starting with snapshots. Think of it as sampling diversity. You could deviate from minima with normal modes, that could be a good way We might end up with MD but should never start there
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AIMD vs MMMD? Per-molecule vs general fit? | PB: Danny Cole says MM MD not useful. Not specifically sure how CB: always general, to avoid overfitting. Unless you want a very bespoke FF
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What’s wrong with straight QM | |
Spitballing plans | |
Practical plans | |
What is the metric for success? | SB: benchmarking infrastructure for other valence refits CB: basic metric for success is alright QM minima, torsion fingerprint, liquid properties Caveat: special corner cases of those Specifically: I think we need to include steric interactions And highly repulsive electrostatics i.e. we need to cater for vdw and electrostatic corner cases Should we group distorted bond lengths, valence angles, etc., and require the FF to work well with those weird corner cases?
DM: there’s asymmetry in how we optimize geometries with QM and get MM to agree with QM
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ELF10 vs not ELF10 | |