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. 2022 Sep 13;18(9):5577-5588.
doi: 10.1021/acs.jctc.2c00311. Epub 2022 Aug 8.

Application of Quantum Chemical Topology Force Field FFLUX to Condensed Matter Simulations: Liquid Water

Benjamin C B Symons  1 , Paul L A Popelier  1
Affiliations

Application of Quantum Chemical Topology Force Field FFLUX to Condensed Matter Simulations: Liquid Water

Benjamin C B Symons et al. J Chem Theory Comput. .

Abstract

We present here the first application of the quantum chemical topology force field FFLUX to condensed matter simulations. FFLUX offers many-body potential energy surfaces learnt exclusively from ab initio data using Gaussian process regression. FFLUX also includes high-rank, polarizable multipole moments (up to quadrupole moments in this work) that are learnt from the same ab initio calculations as the potential energy surfaces. Many-body effects (where a body is an atom) and polarization are captured by the machine learning models. The choice to use machine learning in this way allows the force field's representation of reality to be improved (e.g., by including higher order many-body effects) with little detriment to the computational scaling of the code. In this manner, FFLUX is inherently future-proof. The "plug and play" nature of the machine learning models also ensures that FFLUX can be applied to any system of interest, not just liquid water. In this work we study liquid water across a range of temperatures and compare the predicted bulk properties to experiment as well as other state-of-the-art force fields AMOEBA(+CF), HIPPO, MB-Pol and SIBFA21. We find that FFLUX finds a place amongst these.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Diagram of a single water molecule in FFLUX. MO and MH represent machine learning models. The gradient paths of the electron density are shown for each atom. The dashed black line shows the boundary of the machine learning models (see main text) and should not be confused with a circle marking a cut-off radius. Gradient paths were visualized using AIMSTUDIO.
Figure 2
Figure 2
S-curve of absolute IQA energy prediction errors.
Figure 3
Figure 3
S-curve of absolute charge prediction errors.
Figure 4
Figure 4
Distribution of O–H bond lengths from a 100 ps liquid water simulation. The bond lengths in the 100 randomly sampled dimers are shown by red circles.
Figure 5
Figure 5
S-curves showing the absolute electrostatic errors in the electrostatics for 100 water dimers.
Figure 6
Figure 6
Radial distribution functions (computed using VMD). Top left: oxygen–oxygen, top right: oxygen–hydrogen and bottom: hydrogen–hydrogen. Experimental data taken from Soper. Note that the simulated radial distribution functions include intramolecular contributions whereas the experimental data do not.
Figure 7
Figure 7
Diffusion coefficient computed at various temperatures with FFLUX and other force fields. Experimental data taken from reference.
Figure 8
Figure 8
Density of liquid water computed at various temperatures with FFLUX and other force fields. Experimental data taken from references.,
Figure 9
Figure 9
Thermal expansion coefficient computed at various temperatures with FFLUX and other force fields. Experimental data from reference.
Figure 10
Figure 10
Comparison of enthalpy of vaporization curves computed with various force fields. Experimental data taken from reference.
Figure 11
Figure 11
Comparison of isobaric heat capacity curves computed with various force fields. Experimental data taken from reference.
Figure 12
Figure 12
Comparison of the FFLUX and experimental infrared spectra. Experimental data taken from reference.
See this image and copyright information in PMC

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