Neural network learned Pauli potential for the advancement of orbital-free density functional theory.

The Pauli kinetic energy functional and its functional derivative, termed Pauli potential, play a crucial role in the successful implementation of orbital-free density functional theory for electronic structure calculations. However, the exact forms of these two quantities are not known. Therefore, perforce, one employs the approximate forms for the Pauli functional or Pauli potential for performing orbital-free density functional calculations.

Structural evolution in gold nanoparticles using artificial neural network based interatomic potentials.

Relativistic effects of gold make its behavior different from other metals. Unlike silver and copper, gold does not require symmetrical structures as the stable entities. We present the evolution of gold from a cluster to a nanoparticle by considering a majority of stable structural possibilities.

A transferable artificial neural network model for atomic forces in nanoparticles.

We have designed a new method to fit the energy and atomic forces using a single artificial neural network (SANN) for any number of chemical species present in a molecular system. The traditional approach for fitting the potential energy surface for a multicomponent system using artificial neural network (ANN) is to consider number of networks for number of chemical species in the system. This shoots the computational cost and makes it difficult to apply to a system containing more number of species.

Correlation of structure with UV-visible spectra by varying SH composition in Au-SH nanoclusters.

In the present work, we model artificial neural network (ANN) potentials for Au (SH) nanoclusters in the range of = 10 to = 38. The accuracy of ANN potentials is tested by comparing the global minimum (GM) structures of Au (SH) nanoclusters, at saturated amount of SH, with the earlier reported structures. The GM structures are reported for the first time for nanoclusters with compositions lower than the saturated SH composition.

c-T phase diagram and Landau free energies of (AgAu) nanoalloy via neural-network molecular dynamic simulations.

For understanding the structure, dynamics, and thermal stability of (AgAu) nanoalloys, knowledge of the composition-temperature (c-T) phase diagram is essential due to the explicit dependence of properties on composition and temperature. Experimentally, generating the phase diagrams is very challenging, and therefore theoretical insight is necessary. We use an artificial neural network potential for (AgAu) nanoalloys.

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au nanocluster.

We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au), the computational time for accurate calculation of energy and forces is about 1.

Structural Evolution of Gold Clusters Au (n = 21-25) Revisited.

We performed a combined theoretical and experimental photoelectron spectroscopy study of the structural evolution of gold anion clusters Au in the size range n = 21-25, a special size range for gold anion clusters where extensive structural changes from the pyramidal structure at Au toward the core-shell structure at Au were expected to occur. Density functional theory calculations with inclusion of spin-orbit effects were employed to produce the simulated spectra for the selected low-energy isomers obtained from basin-hopping global minimum search. The comparison of these simulated spectra with reasonably well-resolved experimental photoelectron spectra resulted in the identification of the low-lying structures of the gold clusters.

Neural network potentials for dynamics and thermodynamics of gold nanoparticles.

For understanding the dynamical and thermodynamical properties of metal nanoparticles, one has to go beyond static and structural predictions of a nanoparticle. Accurate description of dynamical properties may be computationally intensive depending on the size of nanoparticle. Herein, we demonstrate the use of atomistic neural network potentials, obtained by fitting quantum mechanical data, for extensive molecular dynamics simulations of gold nanoparticles.