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Bias-Free Chemically Diverse Test Sets from Machine Learning

Swann, Ellen T; Fernandez, Michael; Coote, Michelle; Barnard, Amanda S


Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors. In this work, we compare three sets of descriptors based on the one-,...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Journal article
Source: ACS combinatorial science
DOI: 10.1021/acscombsci.7b00087
Access Rights: Open Access


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