Application of the FFLUX Force Field to Molecular Crystals: A Study of Formamide

13 June 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


In this work we present the first application of the quantum chemical topology force field, FFLUX, to the solid state. FFLUX utilises Gaussian process regression machine learning models trained on data from the interacting quantum atoms 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 optimised using FFLUX. Optimising 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 moment are used. These differences are found to be in line with 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.


Quantum Chemical Topology
Gaussian Process Regression
crystal structure
phonon spectrum
energy-volume curve
multipole moments
Ewald summation
Helmholtz free energy


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