Quipsharp Hessian
%20have%20emerged%20as%20a%20promising%20solution%20for%20constructing%20atomistic%20potentials%20that%20can%20be%20transferred%20across%20different%20molecular%20and%20crystalline%20systems.%20Most%20MLIPs%20are%20trained%20only%20on%20energies%20and%20forces%20in%20vacuum%2C%20while%20an%20improved%20description%20of%20the%20potential%20energy%20surface%20could%20be%20achieved%20by%20including%20the%20curvature%20of%20the%20potential%20energy%20surface.%20We%20present%20Hessian%20QM9%2C%20the%20first%20database%20of%20equilibrium%20configurations%20and%20numerical%20Hessian%20matrices%2C%20consisting%20of%2041%2C645%20molecules%20from%20the%20QM9%20dataset%20at%20the%20%24omega%24B97x%2F6-31G*%20level.%20Molecular%20Hessians%20were%20calculated%20in%20vacuum%2C%20as%20well%20as%20water%2C%20tetrahydrofuran%2C%20and%20toluene%20using%20an%20implicit%20solvation%20model.%20To%20demonstrate%20the%20utility%20of%20this%20dataset%2C%20we%20show%20that%20incorporating%20second%20derivatives%20of%20the%20potential%20energy%20surface%20into%20the%20loss%20function%20of%20a%20MLIP%20significantly%20improves%20the%20prediction%20of%20vibrational%20frequencies%20in%20all%20solvent%20environments%2C%20thus%20making%20this%20dataset%20extremely%20useful%20for%20studying%20organic%20molecules%20in%20realistic%20solvent%20environments%20for%20experimental%20characterization.)
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