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Abstract:
The combination of modern scientific computing with electronic structure theory can lead to an unprecedented
amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and
predictive structure-property relationships. Such relationships enable high-throughput screening for relevant
properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we
present a machine learning (ML) model, trained on a data base of ab initio calculation results for thousands of
organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The
properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron
affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting
underlying correlations between various molecular properties. The input is identical to ab initio methods,
i.e. nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a
“Quantum Machine” is similar, and sometimes superior, to modern quantum-chemical methods—at negligible
computational cost.
