| Autor: |
Brown ML; Department of Chemistry, The University of Manchester, Oxford Road, Manchester M13 9PL, Britain., Skelton JM; Department of Chemistry, The University of Manchester, Oxford Road, Manchester M13 9PL, Britain., Popelier PLA; Department of Chemistry, The University of Manchester, Oxford Road, Manchester M13 9PL, Britain. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of chemical theory and computation [J Chem Theory Comput] 2023 Nov 14; Vol. 19 (21), pp. 7946-7959. Date of Electronic Publication: 2023 Oct 17. |
| DOI: |
10.1021/acs.jctc.3c00578 |
| Abstrakt: |
In this work, we present the first application of the quantum chemical topology force field FFLUX to the solid state. FFLUX utilizes Gaussian process regression machine learning models trained on data from the interacting quantum atom partitioning scheme to predict atomic energies and flexible multipole moments that change with geometry. Here, the ambient (α) and high-pressure (β) polymorphs of formamide are used as test systems and optimized using FFLUX. Optimizing the structures with increasing multipolar ranks indicates that the lattice parameters of the α phase differ by less than 5% to the experimental structure when multipole moments up to the quadrupole are used. These differences are found to be in line with the dispersion-corrected density functional theory. Lattice dynamics calculations are also found to be possible using FFLUX, yielding harmonic phonon spectra comparable to dispersion-corrected DFT while enabling larger supercells to be considered than is typically possible with first-principles calculations. These promising results indicate that FFLUX can be used to accurately determine properties of molecular solids that are difficult to access using DFT, including the structural dynamics, free energies, and properties at finite temperature. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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