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dc.contributor.authorHughes, Zak E.*
dc.contributor.authorThacker, J.C.R.*
dc.contributor.authorWilson, A.L.*
dc.contributor.authorPopelier, P.L.A.*
dc.date.accessioned2019-01-31T17:05:11Z
dc.date.available2019-01-31T17:05:11Z
dc.date.issued2019
dc.identifier.citationHughes ZE, Thacker JCR, Wilson AL and Popelier PLA (2019) Description of Potential Energy Surfaces of Molecules using FFLUX Machine Learning Models. Journal of Chemical Theory and Computation. 15(1): 116-126.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16776
dc.descriptionyesen_US
dc.description.abstractA new type of model, FFLUX, to describe the interaction between atoms has been developed as an alternative to traditional force fields. FFLUX models are constructed from applying the kriging machine learning method to the topological energy partitioning method, Interacting Quantum Atoms (IQA). The effect of varying parameters in the construction of the FFLUX models is analyzed, with the most dominant effects found to be the structure of the molecule and the number of conformations used to build the model. Using these models the optimization of a variety of small organic molecules is performed, with sub kJ mol-1 accuracy in the energy of the optimized molecules. The FFLUX models are also evaluated in terms of their performance in describing the potential energy surfaces (PESs) associated with specific degrees of freedoms within molecules. While the accurate description of PESs presents greater challenges than individual minima, FFLUX models are able to achieve errors of <2.5 kJ mol-1 across the full C-C-C-C dihedral PES of n-butane, indicating the future possibilities of the technique.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1021/acs.jctc.8b00806en_US
dc.rightsThis document is the Accepted Manuscript version of a Published Work that appeared in final form in the Journal of Chemical Theory and Computation, copyright © American Chemical Society, after peer review and technical editing by the publisher. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/acs.jctc.8b00806en_US
dc.subjectMoleculesen_US
dc.subjectPotential energy surfacesen_US
dc.subjectFFLUX modelen_US
dc.subjectForce-fieldsen_US
dc.subjectMolecular modellingen_US
dc.subjectMachine learningen_US
dc.titleDescription of Potential Energy Surfaces of Molecules using FFLUX Machine Learning Modelsen_US
dc.status.refereedYesen_US
dc.date.Accepted2018-11-19
dc.date.application2018-12-03
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2019-01-31T17:07:27Z


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