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We introduce property-independent kernels for machine learning modeling ofarbitrarily many molecular properties. The kernels encode molecular structuresfor training sets of varying size, as well as similarity measures sufficientlydiffuse in chemical space to sample over all training molecules. Correspondingmolecular reference properties provided, they enable the instantaneousgeneration of ML models which can systematically be improved through theaddition of more data. This idea is exemplified for single kernel basedmodeling of internal energy, enthalpy, free energy, heat capacity,polarizability, electronic spread, zero-point vibrational energy, energies offrontier orbitals, HOMO-LUMO gap, and the highest fundamental vibrationalwavenumber. Models of these properties are trained and tested using 112 kiloorganic molecules of similar size. Resulting models are discussed as well asthe kernels' use for generating and using other property models.

Many Molecular Properties from One Kernel in Chemical Space