Skip navigation
Skip navigation

Bias-Free Chemically Diverse Test Sets from Machine Learning

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

Description

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
URI: http://hdl.handle.net/1885/209141
Source: ACS combinatorial science
DOI: 10.1021/acscombsci.7b00087
Access Rights: Open Access

Download

File Description SizeFormat Image
ProtoArch_CCCBDB new.pdf565.05 kBAdobe PDFThumbnail


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator