![]() ![]() Lastly, pyRISM-CNN was successfully applied to the simultaneous prediction of solvation enthalpy, entropy and free energy through a multi-task learning approach, with errors of 1.04, 0.98 and 0.47 kcal mol −1, respectively, for water solvent systems at 298 K. ![]() For neutral solutes, prediction errors nearing or below 1 kcal mol −1 are obtained for each organic solvent system at 298 K and water solvent systems at 273–373 K. Secondly, the number of solvents in the training data has been expanded from carbon tetrachloride, water and chloroform to now also include methanol. ![]() Firstly, solvation free energies have been introduced for organic molecular ions in methanol or water solvent systems at 298 K, with errors below 4 kcal mol −1 obtained without the need for corrections or additional descriptors. Here, we report three further developments to the pyRISM-CNN methodology. In this paper, we propose the federated training on hybrid quantum-classical classifiers. We address these two challenges by providing the framework of training quantum machine learning models in a federated manner. With this approach, a 40-fold improvement in prediction accuracy was delivered for a multi-solvent, multi-temperature dataset when compared to the standard 1D-RISM theory. Another challenge is the rising privacy concern in the use of large scale machine learning infrastructure. Recently, we presented pyRISM-CNN, a combination of the 1 Dimensional Reference Interaction Site Model (1D-RISM) solver, pyRISM, with a deep learning based free energy functional, as a method of predicting solvation free energy (SFE). This has been particularly true for methods from the Integral Equation Theory of Molecular Liquids such as the Reference Interaction Site Model which have traditionally given large errors in solvation thermodynamics. Simultaneous calculation of entropies, enthalpies and free energies has been a long-standing challenge in computational chemistry, partly because of the difficulty in obtaining estimates of all three properties from a single consistent simulation methodology. ![]()
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