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Objective | Develop a plan for a set of studies exploring which data should go into the next main OpenFF refit. | ||||||
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Project Timeline
The tentative timeline for refitting is to spend:
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Study | Status | Project Page | ||||||
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Optimisation with / without $H_{vap}$ while including mixture data. |
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Optimization against $V_{excess}(x) or $\rho(x) |
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How much pure data do we need to include to get good parameterization/Can mixture data alone get energetics right? |
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For mixture properties - does including aqueous mixture data in addition to non-aqueous mixture data improve the overall force field performance (i.e. should we only include non-aqueous data to avoid trying to correct for the water model?). |
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TIP3P vs TIP3P-FB |
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How affected are low-representation molecules by the amount of data? Is there regularization that we need? |
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How reproducible are ForceBalance optimizations with physical properties as targets? If there is an effect, does this effect scale with the number of targets? |
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Medium Priority:
Study | Status | Project Page | ||||
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How do density and hvap individually constraint the parameters? |
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Do OpenEye and RDKit produce AM1-BCC charges which are identical / close enough to not drastically change the FF performance? |
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Study | Status | Project Page | ||||
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What is the difference in fitting between a set where some molecules have only hvap and others have only density, versus a set where all molecules have both hvap and density? |
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Covariance of parameters? |
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A trial run with a sharply reduced set of LJ types? This could reduce the amount of exptl data needed, and Michael Schauperl's results indicate one can do quite nicely with fewer. |
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Explore the change in performance with a split parameter ([#1:1]-[#6X4]-[#6X3]=[#7,#8]), which was identified as a target for splitting in Binary Mixture Data Feasibility Study |
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