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Probing the properties of molecules and complex materials using machine learning
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Funding
The work described in this manuscript was supported by ARC Discovery grants, a DAAD grant, CSIRO internal and postdoctoral fellow funding sources, an Australian Stem Cell Centre postdoctoral fellowship, an EPSRC Next Generation Biomaterials grant and Boeing.
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Publication Date
2022-09-30Journal
Australian Journal of Chemistry: an international journal for chemical scienceVolume
Special IssuePagination
17Publisher
CSIROISSN
0004-9425Rights Statement
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND). This licence allows others to copy and redistribute the material in any medium or format under the following conditions: Attribution – the original source must be cited (BY) NonCommercial – works may not be used for commercial purposes (NC) NoDerivatives – derivates of the article (e.g. translations) may not be distributed (ND)Publisher DOI
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Keywords
machine learningbiomaterialsnanomaterialsporous materials2D materialsartificial intelligencebatteriesBayesian methodscatalystscomplex systemscomputational molecular designdrug designorganic photovoltaic (OPV) devicesquantitative structure-activity relationships (QSAR)regenerative medicinescienceMacromolecular and Materials Chemistry not elsewhere classified
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