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Related papers: Transferability of Zr-Zr interatomic potentials

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Chemical segregation and structural transitions at interfaces are important nanoscale phenomena, making them natural targets for atomistic modeling, yet interatomic potentials must be fit to secondary physical properties. To isolate the…

Materials Science · Physics 2018-02-08 Yang Hu , Jennifer D. Schuler , Timothy J. Rupert

The embedded atom method (EAM) potentials are probably the most widely used interatomic potentials for metals and alloys. However, the EAM potentials impose three constraints on elastic constants that are inconsistent with experiments. At a…

Materials Science · Physics 2015-06-11 L. G. Zhou , Hanchen Huang

The parameters of many-body potentials for Co, Nb and Zr metals, based on the embedded-atom method, have been systematically derived. The analytical potential scheme allows us to reproduce correctly the cohesive energies and structural…

Materials Science · Physics 2014-05-02 Pascal Thibaudeau , Julian Gale

In order to study the performance of interatomic potentials and their reliability at higher pressures, the phase diagram of four different embedded-atom type potential models of iron is compared. The calculations were done by the nested…

Materials Science · Physics 2018-03-23 Livia B. Partay

Interatomic potentials have been widely used in atomistic simulations such as molecular dynamics. Recently, frameworks to construct accurate interatomic potentials that combine a systematic set of density functional theory (DFT)…

Materials Science · Physics 2015-09-09 Atsuto Seko , Akira Takahashi , Isao Tanaka

Large scale atomistic simulations with suitable interatomic potentials are widely employed by scientists or engineers of different areas. Quick generation of high-quality interatomic potentials is of urgent need under present circumstances,…

Materials Science · Physics 2016-11-23 Kun Wang , Wenjun Zhu , Shifang Xiao , Jun Chen , Wangyu Hu

Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training…

Materials Science · Physics 2025-07-30 Mariia Radova , Wojciech G. Stark , Connor S. Allen , Reinhard J. Maurer , Albert P. Bartók

Interatomic potentials approximate the potential energy of atoms as a function of their coordinates. Their main application is the effective simulation of many-atom systems. Here, we review empirical interatomic potentials designed to…

Materials Science · Physics 2022-11-11 Martin H. Muser , Sergey V. Sukhomlinov , Lars Pastewka

An interatomic potential (termed EAM-21) has been developed with the embedded atomic method (EAM) for CrFeMnNi quaternary HEAs. This potential is based on a previously developed potential for CrFeNi ternary alloys. The parameters to develop…

We develop and compare four interatomic potentials for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dependent terms, a potential with machine-learned EAM and…

A novel embedded atom method (EAM) potential for the Xi-phases of Al-Pd-Mn has been determined with the force-matching method. Different combinations of analytic functions were tested for the pair and transfer part. The best results are…

Materials Science · Physics 2012-02-09 Daniel Schopf , Peter Brommer , Benjamin Frigan , Hans-Rainer Trebin

Choice of appropriate force field is one of the main concerns of any atomistic simulation that needs to be seriously considered in order to yield reliable results. Since, investigations on mechanical behavior of materials at micro/nanoscale…

Computational Physics · Physics 2016-07-12 Seyed Moein Rassoulinejad-Mousavi , Yijin Mao , Yuwen Zhang

We developed new modified embedded-atom method (MEAM) interatomic potentials for the Mg-Al alloy system using a first-principles method based on density functional theory (DFT). The materials parameters, such as the cohesive energy,…

Materials Science · Physics 2013-05-29 B. Jelinek , J. Houze , Sungho Kim , M. F. Horstemeyer , M. I. Baskes , Seong-Gon Kim

Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure…

Materials Science · Physics 2022-08-15 Michele Ceriotti

Large-scale simulations of plastic deformation and phase transformations in alloys require reliable classical interatomic potentials. We construct an embedded-atom method potential for niobium as the first step in alloy potential…

Materials Science · Physics 2010-04-27 Michael R. Fellinger , Hyoungki Park , John W. Wilkins

We show that a deep-learning neural network potential (DP) based on density functional theory (DFT) calculations can well describe Cu-Zr materials, an example of a binary alloy system that can coexist in several ordered intermetallics and…

Materials Science · Physics 2020-04-29 Christopher M. Andolina , Philip Williamson , Wissam A. Saidi

We propose a modification of the embedded-atom method-type potential aiming at reconciling simulated melting and ground-state properties of metals by means of classical molecular dynamics. Considering titanium, magnesium, gold, and platinum…

Mesoscale and Nanoscale Physics · Physics 2016-04-15 Gennady Sushko , Alexey Verkhovtsev , Christian Kexel , Andrei V. Korol , Stefan Schramm , Andrey V. Solov'yov

The most critical limitation to the wide-scale use of classical molecular dynamics for alloy design is the availability of suitable interatomic potentials. In this work, we demonstrate a simple procedure to generate a library of accurate…

Materials Science · Physics 2012-09-05 Logan Ward , Anupriya Agrawal , Katharine M. Flores , Wolfgang Windl

Semi-empirical interatomic potentials have been developed for Al, alpha-Ti, and gamma-TiAl within the embedded atomic method (EAM) by fitting to a large database of experimental as well as ab-initio data. The ab-initio calculations were…

Materials Science · Physics 2009-11-10 Rajendra R. Zope , Y. Mishin

The length and time scales of atomistic simulations are limited by the computational cost of the methods used to predict material properties. In recent years there has been great progress in the use of machine learning algorithms to develop…

Computational Physics · Physics 2022-11-03 Alberto Hernandez , Adarsh Balasubramanian , Fenglin Yuan , Simon Mason , Tim Mueller
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