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Free keywords:
machine learning;
quantum chemistry;
tutorial;
kernel ridge regression;
implementation
Abstract:
Models that combine quantum mechanics (QM) with machine learning (ML) promise to deliver the accuracy of QM at the speed of ML. This hands-on tutorial introduces the reader to QM/ML models based on kernel learning, an elegant, systematically nonlinear form of ML. Pseudocode and a reference implementation are provided, enabling the reader to reproduce results from recent publications where atomization energies of small organic molecules are predicted using kernel ridge regression
