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    Description of Potential Energy Surfaces of Molecules using FFLUX Machine Learning Models

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    Publication date
    2019
    Author
    Hughes, Zak E.
    Thacker, J.C.R.
    Wilson, A.L.
    Popelier, P.L.A.
    Keyword
    Molecules
    Potential energy surfaces
    FFLUX model
    Force-fields
    Molecular modelling
    Machine learning
    Rights
    This 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.8b00806
    Peer-Reviewed
    Yes
    
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    Abstract
    A 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.
    URI
    http://hdl.handle.net/10454/16776
    Version
    Accepted Manuscript
    Citation
    Hughes 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.
    Link to publisher’s version
    https://doi.org/10.1021/acs.jctc.8b00806
    Type
    Article
    Collections
    Life Sciences Publications

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