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Description of Potential Energy Surfaces of Molecules using FFLUX Machine Learning Models
Thacker, J.C.R. Wilson, A.L. Popelier, P.L.A.
Thacker, J.C.R.
Wilson, A.L.
Popelier, P.L.A.
Publication Date
2019
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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
Open Access status
openAccess
Accepted for publication
19/11/2018
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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.
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.
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Article