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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…

Materials Science · Physics 2023-07-19 L. Tang , Z. J. Yang , T. Q. Wen , K. M. Ho , M. J. Kramer , C. Z. Wang

We present results of electronic structure calculations for well-relaxed Au/benzene-1,4-dithiol/Au molecular contacts, based on density functional theory and the generalized gradient approximation. Electronic states in the vicinity of the…

Mesoscale and Nanoscale Physics · Physics 2009-11-11 Udo Schwingenschloegl , Cosima Schuster

Multicomponent nitrides are a hot research topic in the search of hard coatings. The effect of substitutions on the phase stabilities, magnetic, and elastic properties of $Al_{1-x-y}Cr_{x}Ti_{y}N$ $(0\leq x,y\leq1)$ was studied using first…

An understanding of the phase diagram of elemental plutonium (Pu) must include both the effects of the strong directional bonding and the high density of states of the Pu 5f electrons, as well as how that bonding weakens under the influence…

Strongly Correlated Electrons · Physics 2015-06-18 Jian-Xin Zhu , R. C. Albers , K. Haule , G. Kotliar , J. M. Wills

Silver interlayers have been shown to enable smooth lithium deposition and cycling in anode-free solid-state batteries. Here, we report the atomic structure of the Ag and Li interface, showing that Li preferentially plates as FCC on both…

Materials Science · Physics 2026-02-13 Grace M. Lu , Dallas R. Trinkle

By fitting to a database of ab-initio forces and energies, we can extract pair potentials for alloys, with a simple six-parameter analytic form including Friedel oscillations, which give a remarkably faithful account of many complex…

Materials Science · Physics 2015-05-30 M. Mihalkovic , C. L. Henley

Li-based half-Heusler alloys have attracted much attention due to their potential applications in optoelectronics and because they carry the possibility of exhibiting large magnetic moments for spintronic applications. Due to their…

Materials Science · Physics 2015-03-05 L. Damewood , B. Busemeyer , M. Shaughnessy , C. Y. Fong , L. H. Yang , C. Felser

Using the relativistic all-electron density-functional calculations on the AuN (N=2-26) in the generalized gradient approximation, combined with the guided simulated annealing, we have found that the two- to three-dimensional structural…

Materials Science · Physics 2007-05-23 Wei Fa , Chuanfu Luo , Jinming Dong

Taking into considerations the wide compositional stretch of Heusler alloys, the first principles density functional theory based calculations are excellently suitable for estimating the multifaceted properties of alkali metal based LiVSb…

Materials Science · Physics 2024-07-03 Diwaker , Shyam L. Gupta , Anupam , Sumit Kumar , Aadil Fayaz , Ashwani Kumar

Phase diagram of Np-U-Zr, a key ternary alloy system of relevance for metallic nuclear fuels, is still largely undetermined. Here a thermodynamic model for the Np-U-Zr system is developed based on Muggianu extrapolation of our models for…

Materials Science · Physics 2017-12-13 Wei Xie , Dane Morgan

A reliable prediction of interatomic force constants in disordered alloys is an outstanding problem. This is due to the need for a proper treatment of multisite (atleast pair) correlation within a random environment. The situation becomes…

Materials Science · Physics 2014-04-14 Rajiv K. Chouhan , Aftab Alam , Subhradip Ghosh , Abhijit Mookerjee

We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating…

The water/electrode interface under an applied bias potential is a challenging out-of-equilibrium phenomenon, which is difficult to accurately model at the atomic scale. In this study, we employ a combined approach of Density Functional…

Materials Science · Physics 2025-03-14 Graciele M. Arvelos , Marivi Fernández-Serra , Alexandre R. Rocha , Luana S. Pedroza

Ti-N material system have range of compounds with different stoichiometry like Ti2N, Ti3N2, Ti6N5, Ti4N3 alongwith Ti , TiN and solid solutions of N in Ti with a maximum of 23% solubility. In this work, we develop an interatomic potential…

Materials Science · Physics 2025-07-28 Pradeep Kumar Rana , Atharva Vyawahare , Rohit Batra , Satyesh Kumar Yadav

The structural properties of the uranium-encapsulated nano-cage U@Au14 are predicted using density functional theory. The presence of the uranium atom makes the Au14 structure more stable than the empty Au14-cage, with a triplet ground…

Atomic and Molecular Clusters · Physics 2013-12-30 Yang Gao , Xing Dai , Seung-gu Kang , Camilo Andres Jimenez Cruz , Minsi Xin , Yan Meng , Jie Han , Zhigang Wang , Ruhong Zhou

The design of novel cathode materials for Li-ion batteries would greatly benefit from accurate first-principles predictions of structural, electronic, and magnetic properties as well as intercalation voltages in compounds containing…

Materials Science · Physics 2022-11-03 Iurii Timrov , Francesco Aquilante , Matteo Cococcioni , Nicola Marzari

Embedded density functional theory (e-DFT) is used to describe the electronic structure of strongly interacting molecular subsystems. We present a general implementation of the Exact Embedding (EE) method [J. Chem. Phys. 133, 084103 (2010)]…

Other Condensed Matter · Physics 2011-07-27 Jason D. Goodpaster , Taylor A. Barnes , Thomas F. Miller

We present our findings of a large-scale screening for new synthesizable materials in five M-Sn binaries, M = Na, Ca, Cu, Pd, and Ag. The focus on these systems was motivated by the known richness of M-Sn properties with potential…

Materials Science · Physics 2025-07-10 Aidan Thorn , Daviti Gochitashvili , Saba Kharabadze , Aleksey N. Kolmogorov

Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, Machine learning interatomic potentials (MLIP) can accurately reproduce first-principles…

Materials Science · Physics 2024-03-01 Sasaank Bandi , Chao Jiang , Chris A. Marianetti

The use of machine learning interatomic potentials (MLIPs) in simulations of materials is a state-of-the-art approach, which allows achieving nearly \textit{ab initio} accuracy with orders of magnitude less computational cost.…

Materials Science · Physics 2021-10-28 R. E. Ryltsev , N. M. Chtchelkatchev