Related papers: Transferability of Zr-Zr interatomic potentials

Machine learned interatomic potentials (MLIPs) are becoming a standard method for DFT-level accurate molecular dynamics simulation and large-scale studies of crystal energetics. Increasingly popular are universal pre-trained potentials,…

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a…

We have looked into the structural, mechanical, optoelectronic, superconducting state and thermophysical aspects of intermetallic compound Zr$_3$Ir using the density functional theory (DFT). Many of the physical properties, including…

Molecular dynamics simulation is employed to understand the thermodynamic behavior of cuboctahedron (cub) and icosahedron (ico) nanoparticles with 2-20 number of shells (55-28741 atoms). The embedded atom method was used to describe the…

We studied acetylhistidine (AcH), bare or microsolvated with a zinc cation by simulations in isolation. First, a global search for minima of the potential energy surface combining both, empirical and first-principles methods, is performed…

An extensive set of fine structure levels and corresponding transition probabilities for allowed and forbidden transitions in Fe XVII is presented. A total of 490 bound energy levels of Fe XVII of total angular momenta 0 <= J <= 7 of even…

Electrochemical water electrolysis, which produces clean energy carriers to mitigate carbon emissions, lacks suitable, low-cost electrodes for efficient oxygen evolution reaction (OER) in alkaline water splitting. To address this challenge,…

The rapid development of universal machine learning interatomic potentials (uMLIPs) has demonstrated the possibility for generalizable learning of the universal potential energy surface. In principle, the accuracy of uMLIPs can be further…

An interatomic potential for Al-Tb alloy around the composition of Al90Tb10 was developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom…

For a given many-electron molecule, it is possible to define a corresponding one-electron Schr\"odinger equation, using potentials derived from simple atomic densities, whose solution predicts fairly accurate molecular orbitals for single-…

We extend our recently developed interatomic potentials for UO_{2} to the mixed oxide fuel system (U,Pu,Np)O_{2}. We do so by fitting against an extensive database of ab initio results as well as to experimental measurements. The…

Finding new materials with previously unknown atomic structure or materials with optimal set of properties for a specific application greatly benefits from computational modeling. Recently, such screening has been dramatically accelerated…

For the family of model soft Coulomb potentials represented by V(r) = -\frac{Z}{(r^q+\beta^q)^{\frac{1}{q}}}, with the parameters Z>0, \beta>0, q \ge 1, it is shown analytically that the potentials and eigenvalues, E_{\nu\ell}, are…

The concept of z-scaling reflecting the general features of internal particle substructure, constituent interaction and mechanism of particle formation at high-pT is reviewed. Experimental data on inclusive cross sections obtained at U70,…

In the present work we detail how the many-body potential energy landscape of interatomic potentials for carbon can be explored by utilising the nested sampling algorithm, allowing the calculation of their pressure-temperature phase diagram…

UZr based metallic nuclear fuel is the leading candidate for next-generation sodium-cooled fast reactors in the United States. US research reactors have been using and testing this fuel type since the 1960s and accumulated considerable…

Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus…

In this work we developed a practical and general modeling approach for thermal conductivity of metals and metal alloys that integrates ab initio and semiempirical physics-based models to maximize the strengths of both techniques. The…

Here we explore the atomic number ($Z$) dependence of multislice electron ptychography and approaches to optimize Z sensitivity. Specifically, we show that ptychography's $Z$-dependence is highly dependent on the integrated area of an atom…

The embedded atom method (EAM) is one of the most widely used many-body, short-range potentials in molecular dynamics simulations, particularly for metallic systems. To enhance the efficiency of calculating these short-range interactions,…