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  • In general, to what extent are LJ parameters trained on mixture dafile:/Users/boothros/PyCharmProjects/nistdataselection/studies/mixture_feasibility/benchmarking/data_set_generation/expanded_set/test_sets/density_binary_qwffqewfe.pdf



    file:/Users/boothros/PyCharmProjects/nistdataselection/studies/mixture_feasibility/benchmarking/data_set_generation/expanded_set/test_sets/pure_set_egfsefgse.pdfta ta transferable to other sets of mixture?

    • The first mixture benchmark set (MB1), consisting of heat of vaporization, mixture density, and excess molar volume of alcohol/ester, alcohol/alcohol, alcohol/acid, ester/acid, acid/acid and alcohol/ether mixtures that do not have any commonality to the mixtures that we trained on.

  • If we train LJ parameters on one type of mixture, how well do those parameters transfer to other types of mixture?

    • Since we included only alcohol/ester mixtures in our training data, MB1 will allow us to look at the transferability of LJ parameters to other types of mixtures.

      • Alcohol/alcohol mixtures: We train on mixtures that contain alcohols, but only mixtures of alcohol with esters. To what extent do the alcohol LJ parameter transfer to mixtures that only include alcohols.

      • Alcohol/ether mixtures: To what extent do the LJ parameters for alcohols, trained on ester mixtures, transfer to mixtures of alcohols and ethers (which have not been trained on at all)? Ethers should be similar to esters, so if they do quite poorly, this will be an issue.

  • How do benchmarked mixture properties vary as a function of composition?

    • Through benchmarking, can we identify the extent that transferability affects mixture properties as a function of composition? For example, if we test against alcohol/ether mixtures and we see worse performance as the ether concentration increases, then maybe the alcohol parameters are good, but the ester parameters don’t transfer well to ethers.

    • MB1 will allow us to explore this, since we should have good coverage in a range of xA=0.2-0.8. We should also consider adding some points in the 0.9-0.95 mole fraction region, to check on that behavior. This could either be as a separate set, or just something we break out in MB1

  • Can we identify a “spectrum of transferability” for parameters in mixtures.

    • For example, within a benchmark set composed of mixtures that have at least one component in the training set (MB2), there are a large number of mixture with tert-butanol. Assuming that tert-butanol is parameterized reasonably well, by examining the mixture properties and chemical similarity of the other moieties in these tert-butanol sets, can we identify how different a mixture can be before the transferability starts to degrade?

  • To what extent are mixture properties transferable from training on pure properties only? To what extent are pure properties transferable from training on mixture properties only?

    • Can we get a sense of the correlation between performance on mixture properties and pure properties (does low error in pure density imply low error in mixture density)? By benchmarking sets parameterized with only pure and only mixture data on MB1 and PB1 (the basic pure data benchmark set), we can analyze this.

  • How do mixtures trained on mixture densities perform on excess molar volumes?

    • By looking at the subset of MB1 that includes excess molar volumes, can we accurately reproduce these by training on mixture densities? If we can’t, that may point to excess molar volumes not being very useful for us.