Participants
Goals
Discussion topics
Recording: https://us06web.zoom.us/rec/share/OHO18PMDQpSyl5W3iukkX2EMcSa8V1oAlN9O6UpeSiVplA8zwM8ccCU6wvqXYKpg.QPfzFJEsxEr1cItj
Passcode: E+3mAmF5
Item | Presenter | Notes |
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mixture properties in water mixtures | Barara Morales | MS: regressing to previous infrastructure has been problematic in running SFEs Looking at Hmix, density for 7 water models, wants to look at SFE but infra issues Binary mixtures with water, using Sage 2.1 TIP3P “best” water model for alcohols, but still high error TIP4P/TIP3P best for amines and both alc/amine DM: Looks like confidence intervals overlap for different RMSE’s, seems hard to tell which is “best” based on RMSE [MS/BM agree] DM: May find that another analysis would be helpful, maybe paired t-test, to see statistics on by-molecule basis rather than aggregating over whole dataset [recording around 10:30--not confident I captured this correctly] Want to re-train LJ with TIP3P_FB and OPC3 or maybe 4-point model, and check OpenFF 1.0 JW: I would think OPC would be better than TIP4P, but you see OPC is the worst--is that expected? DM: why is r2 negative? BM: happens when correlation is really bad, using scikit, docs just said it represents very bad correlation [https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html ] DM: thought this would be pearson R, which should always be positive, do you know which r measure it is? BM: I’ll look into it MS: May be worth doing the fit with your own code DM: looks like this is a coefficient of determination
MS: reason we want to try Parsley 1.3 is because it’s before we re-trained our LJ parameters with TIP3P, so other models might be better BS: could also train charges MS: also want to try SFE’s with nonpolar molecules MS: goal of this is: should we change our water model? If TIP3P FB and TIP4P are already close to TIP3P, maybe if we reopt LJ for these models, they’d perform even better MS: doesn’t look like 4 point models are better BS: depends what you want--TIP3P doesn’t have some properties MS: yes, TIP4P is more close to real water. TIP3P FB and OPC3 are closer to 4-point model performance than TIP3P BS: TIP4P doesn’t give right density using Ewald methodologies MS: some of that is corrected in TIP3P FB and OPC3
JW: I see switching the water model as something we want to do, but only once. Would make sense to align timeline-wise with release of protein FF. Additionally, could think about making our own water model, I think the offxml environment is so alien to people that they won’t care if it’s an existing water model or not MS: do we want to have an intermediate where we recommend an existing water model? DM: don’t think it matters, as long as it’s good. Don’t think people will refuse to use our water model JW: Disagree, I think it’s much easier to release it as one release rather than going through intermediates DM: sure, I mostly meant I don’t think it matters if we also support another water model. People will use whatever we say to use
CC: Some molecules will exist as charged species in water (eg primary amines), are you doing anything to account for that BM: no, should look at that CC: I don’t think we did either for Sage, kind of a gap MS: Need to look at pKa and see if it’s near pH 7 CC: I think for primary amines, they will be MS: Not just pKa but how does it change as a result of composition BS: have some notes about treating this, who should i send it to MS: either put in slack for everyone or email to me and I’ll forward it MS: Is there a good pKa predictor for small molecules, or do you have to do QM? BS: probably has been measured DM: if not, pKa prediction is really hard PB: qupKake could work https://pubs.acs.org/doi/10.1021/acs.jctc.4c00328
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Slow diffusion in lipid simulations | Julianne Hoeflich | Overall slow diffusion in lipid simulations, much slower lateral diffusion than MacRog and Slipids Think it’s due to alkane tail behavior Lipid tails are 6-18 C Neither Slipids nor MacRog uses HMR Calculate D from simulations; Sage 2.1, HMR is slower than non-HMR but both are slower than expt JW: to be clear, even with small tail length, still have head groups with ~10 heavy atoms? MS: HMR is reducing diffusion constant, COM not affected by HMR but dynamics of things twisting/rotating are affected/slowed down due to moving moment of inertia JH: Amber’s most recent lipid FF mentions they have to fine tune C-C-C angle for alkanes, which drastically affected lipid diffusion, after tuning the angle they re-trained torsions which helped a lot D underestimated worse as chain length grows; up to 20% of the diffusion constant for 15 C Density is pretty accurate TIP3P has results you’d expect for D and density, suggesting it’s not the problem BS: you left out TIP3P D, it’s 3, you predict 6… JH: yeah, it’s true. but we’re looking at alkanes for now, shouldn’t affect it too much
Diffusion
and mixture properties | Shirts group | One torsion shape
| BWdoes not always increase with box size as it would be expected to do, not sure if that’s OK Next steps: re-fit angles/torsions for CCC, then re-run and see if it increases D maybe use QM data or expand dataset, existing torsions aren’t really trained to linear alkanes
TG: If you’re going to do angles, I’d suggest splitting C-C-C vs C-C-H. Currently combined JH: why would those be together…? TG: doesn’t look super different/worth splitting, but I’ve found it’s important BW: I think we tried splitting this and didn’t see much effect?
LM: Maybe didn’t affect RMSD/ddE but would affect other things?
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One torsion shape
| BW
| MS: if angles are so dominant, does it mean it’s not properly minimized? JW: usually high angle/vdW would mean it’s a sterics clash SMIRKS string is [#6X3:1]=[#7X2,#7X3+1:2]-[#6X4:3]-[#6X3,#6X4:4], C-NX3-C-C BS: are you sure dark blue dotted line is angle and not vdW 1-4?
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Action items
Decisions