CCS: A software framework to generate two-body potentials using Curvature Constrained Splines

Authors: Akshay Krishna A. K., Eddie Wadbro, Christof Köhler, Pavlin Mitev, Peter Broqvist, and Jolla Kullgren

We have developed an automated and efficient scheme for the fitting of data using Curvature Constrained Splines (CCS), to construct accurate two-body potentials. The approach enabled the construction of an oscillation-free, yet flexible, potential. We show that the optimization problem is convex and that it can be reduced to a standard Quadratic Programming (QP) problem. The improvements are demonstrated by the development of a two-body potential for Ne from ab initio data. We also outline possible extensions to the method.

Program summary
Program Title: CCS

CPC Library link to program files: http://dx.doi.org/10.17632/7dt5nzxgbs.1

Developer’s repository link: http://github.com/aksam432/CCS

Licensing provisions: GPLv3

Programming language: Python

External routines/libraries: NumPy, matplotlib, ASE, CVXOPT

Nature of problem: Ab initio quantum chemistry methods are often computationally very expensive. To alleviate this problem, the development of efficient empirical and semi-empirical methods is necessary. Two-body potentials are ubiquitous in empirical and semi-empirical methods.

Solution method: The CCS package provides a new strategy to obtain accurate two body potentials. The potentials are described as cubic splines with curvature constraints.

Computer Physics Communications, 258, 107602, (2021);

https://doi.org/10.1016/j.cpc.2020.107602

The water/ceria(111) interface: Computational overview and new structures

Authors: Andreas Röckert, Jolla Kullgren, Peter Broqvist, Seif Alwan, and Kersti Hermansson
 
Thin film structures of water on the CeO2(111) surface for coverages between 0.5 and 2.0 water monolayers have been optimized and analyzed using density functional theory (optPBE-vdW functional). We present a new 1.0 ML structure that is both the lowest in energy published and features a hydrogen-bond network extending the surface in one-dimension, contrary to what has been found in the literature, and contrary to what has been expected due to the large bulk ceria cell dimension. The adsorption energies for the monolayer and multilayered water structures agree well with experimental temperature programmed desorption results from the literature, and we discuss the stability window of CeO2(111) surfaces covered with 0.5–2.0 ML of water.
 

Quantitative and qualitative performance of density functional theory rationalized by reduced density gradient distributions

Authors: Ageo Meier de Andrade, Jolla Kullgren and Peter Broqvist 

We evaluate the qualitative and quantitative accuracy of various flavors of density functionals with and without accounting for dispersion corrections. Our test system is nickel in the form of bulk, surfaces, and nanoparticles for which we compute structural properties, bulk cohesive energies, surface energies, and work functions and compare to experimental data. We find that the inclusion of any dispersion, either by an a posteriori correction or by a self-consistent treatment by explicitly computing the nonlocal correlation contribution to the total energy, has a significant effect on the calculated properties and improves the quantitative comparison to experiments. Besides the quantitative agreement, we also investigate qualitative features by comparing Wulff shapes of metal nanoparticles as obtained using the different density functionals. We find that all tested functionals predict similar Wulff shapes for nickel nanoparticles but still have some small differences. These results show that the relative energies calculated using the semilocal GGA and meta-GGA functionals, with and without dispersion, are quite similar. Our findings can also be generalized to other systems when rationalized in terms of the computed reduced density gradients. We find that the distribution of reduced density gradients in a material is correlated to the steepness of the exchange enhancement factor and propose that this information can be used as a quantitative guide when it comes to picking the most appropriate density functional for specific target systems as well as when it comes to extrapolating DFT data to predict experiments.

Phys. Rev. B 102, 2020, 075115

https://doi.org/10.1103/PhysRevB.102.075115

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