Search

We present a Δ-machine learning model for obtaining Kohn–Sham accuracy from orbital-free density functional theory (DFT) calculations. In particular, we employ a machine-learned force field (MLFF) scheme based on the kernel method to capture the difference between Kohn–Sham and orbital-free DFT energies/forces. We implement this model in the context of on-the-fly molecular dynamics simulations and study its accuracy, performance, and sensitivity to parameters for representative systems. We find that the formalism not only improves the accuracy of Thomas–Fermi–von Weizsäcker orbital-free energies and forces by more than two orders of magnitude but is also more accurate than MLFFs based solely on Kohn–Sham DFT while being more efficient and less sensitive to model parameters. We apply the framework to study the structure of molten Al0.88Si0.12, the results suggesting no aggregation of Si atoms, in agreement with a previous Kohn–Sham study performed at an order of magnitude smaller length and time scales. [ABSTRACT FROM AUTHOR]

Recently, we have proposed the adaptive local basis set for electronic structure calculations based on Kohn–Sham density functional theory in a pseudopotential framework. The adaptive local basis set is efficient and systematically improvable for total energy calculations. In this paper, we present the calculation of atomic forces, which can be used for a range of applications such as geometry optimization and molecular dynamics simulation. We demonstrate that, under mild assumptions, the computation of atomic forces can scale nearly linearly with the number of atoms in the system using the adaptive local basis set. We quantify the accuracy of the Hellmann–Feynman forces for a range of physical systems, benchmarked against converged planewave calculations, and find that the adaptive local basis set is efficient for both force and energy calculations, requiring at most a few tens of basis functions per atom to attain accuracies required in practice. Since the adaptive local basis set has implicit dependence on atomic positions, Pulay forces are in general nonzero. However, we find that the Pulay force is numerically small and systematically decreasing with increasing basis completeness, so that the Hellmann–Feynman force is sufficient for basis sizes of a few tens of basis functions per atom. We verify the accuracy of the computed forces in static calculations of quasi-1D and 3D disordered Si systems, vibration calculation of a quasi-1D Si system, and molecular dynamics calculations of H 2 and liquid Al–Si alloy systems, where we show systematic convergence to benchmark planewave results and results from the literature. [ABSTRACT FROM AUTHOR]

Lithium-ionbattery performance is strongly influenced by the ionicconductivity of the electrolyte, which depends on the speed at whichLi ions migrate across the cell and relates to their solvation structure.The choice of solvent can greatly impact both the solvation and diffusivityof Li ions. In this work, we used first-principles molecular dynamicsto examine the solvation and diffusion of Li ions in the bulk organicsolvents ethylene carbonate (EC), ethyl methyl carbonate (EMC), anda mixture of EC and EMC. We found that Li ions are solvated by eithercarbonyl or ether oxygen atoms of the solvents and sometimes by thePF6–anion. Li+prefers a tetrahedrally coordinated firstsolvation shell regardless of which species are involved, with thespecific preferred solvation structure dependent on the organic solvent.In addition, we calculated Li diffusion coefficients in each electrolyte,finding slightly larger diffusivities in the linear carbonate EMCcompared to the cyclic carbonate EC. The magnitude of the diffusioncoefficient correlates with the strength of Li+solvation.Corresponding analysis for the PF6–anion shows greater diffusivity associatedwith a weakly bound, poorly defined first solvation shell. These resultscan be used to aid in the design of new electrolytes to improve Li-ionbattery performance. [ABSTRACT FROM AUTHOR]