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Quantum Machine Learning in Chemical Compound Space
Author(s) -
von Lilienfeld O. Anatole
Publication year - 2018
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.201709686
Subject(s) - quantum machine learning , quantum chemical , property (philosophy) , quantum chemistry , computer science , quantum , space (punctuation) , molecule , artificial intelligence , statistical physics , machine learning , quantum mechanics , quantum algorithm , physics , philosophy , supramolecular chemistry , epistemology , operating system
Rather than numerically solving the computationally demanding equations of quantum or statistical mechanics, machine learning methods can infer approximate solutions, interpolating previously acquired property data sets of molecules and materials. The case is made for quantum machine learning: An inductive molecular modeling approach which can be applied to quantum chemistry problems.