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Related papers: Transferability of Zr-Zr interatomic potentials

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Machine learning (ML) based interatomic potentials are emerging tools for materials simulations but require a trade-off between accuracy and speed. Here we show how one can use one ML potential model to train another: we use an existing,…

Materials Science · Physics 2022-09-20 Joe D. Morrow , Volker L. Deringer

A modification of an embedded-atom method (EAM)-type potential is proposed for a quantitative description of equilibrium and non-equilibrium properties of metal systems within the molecular-dynamics framework. The modification generalizes…

Mesoscale and Nanoscale Physics · Physics 2021-07-09 Alexey Verkhovtsev , Andrei V. Korol , Gennady Sushko , Stefan Schramm , Andrey V. Solov'yov

Accuracy of molecular dynamics simulations depends crucially on the interatomic potential used to generate forces. The gold standard would be first-principles quantum mechanics (QM) calculations, but these become prohibitively expensive at…

New interatomic potentials describing defects, plasticity and high temperature phase transitions for Ti are presented. Fitting the martensitic hcp-bcc phase transformation temperature requires an efficient and accurate method to determine…

Materials Science · Physics 2016-12-12 M. I. Mendelev , T. L. Underwood , G. J. Ackland

New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design from atomistic simulations is limited by interatomic…

Materials Science · Physics 2026-03-05 Jesper Byggmästar , Tiago Lopes , Zheyong Fan , Tapio Ala-Nissila

Based on an analysis of the short range chemical environment of each atom in a system, standard machine learning based approaches to the construction of interatomic potentials aim at determining directly the central quantity which is the…

Materials Science · Physics 2015-08-05 S. Alireza Ghasemi , Albert Hofstetter , Santanu Saha , Stefan Goedecker

Machine-learning models in chemistry - when based on descriptors of atoms embedded within molecules - face essential challenges in transferring the quality of predictions of local electronic structures and their associated properties across…

Chemical Physics · Physics 2024-09-27 Frederik Ø. Kjeldal , Janus J. Eriksen

A Moment Tensor Potential (MTP) has been developed for the Cu-Ag binary alloy and its accuracy, transferability, and thermodynamic fidelity evaluated. The model was trained on a diverse dataset encompassing solid, liquid, and interfacial…

Materials Science · Physics 2025-08-26 Mashroor S. Nitol , Marco J. Echeverría Iriarte , Doyl E. Dickel , Saryu J. Fensin

Laser ablation is often explained by a two-temperature model (TTM) with different electron and lattice temperatures. To realize a classical molecular dynamics simulation of the TTM, we propose an extension of the embedded atom method to…

Materials Science · Physics 2022-03-09 Yuta Tanaka , Shinji Tsuneyuki

In this work, we developed an interatomic potential for saturated hydrocarbons using the modified embedded-atom method (MEAM), a reactive semi-empirical many-body potential based on density functional theory and pair potentials. We…

Chemical Physics · Physics 2017-09-13 S. Nouranian , M. A. Tschopp , S. R. Gwaltney , M. I. Baskes , M. F. Horstemeyer

The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and…

Computational Physics · Physics 2019-06-26 Mitchell A. Wood , Mary Alice Cusentino , Brian D. Wirth , Aidan P. Thompson

We propose a simple scheme to construct composition-dependent interatomic potentials for multicomponent systems that when superposed onto the potentials for the pure elements can reproduce not only the heat of mixing of the solid solution…

Materials Science · Physics 2012-01-31 B. Sadigh , P. Erhart , A. Stukowski , A. Caro

Gaussian Process Regression-based Gaussian Approximation Potential has been used to develop machine-learned interatomic potentials having density-functional accuracy for free sodium clusters. The training data was generated from a large…

Atomic and Molecular Clusters · Physics 2023-09-19 Balasaheb J. Nagare , Sajeev Chacko , Dilip. G. Kanhere

We develop an empirical potential for silicon which represents a considerable improvement over existing models in describing local bonding for bulk defects and disordered phases. The model consists of two- and three-body interactions with…

Materials Science · Physics 2016-08-31 Joao F. Justo , Martin Z. Bazant , Efthimios Kaxiras , V. V. Bulatov , Sidney Yip

Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics…

Materials Science · Physics 2025-11-21 Fraser Birks , Matthew Nutter , Thomas D Swinburne , James R Kermode

We present a general-purpose parameterization of the atomic cluster expansion (ACE) for magnesium. The ACE shows outstanding transferability over a broad range of atomic environments and captures physical properties of bulk as well as…

Materials Science · Physics 2023-05-08 Eslam Ibrahim , Yury Lysogorskiy , Matous Mrovec , Ralf Drautz

The capacities of various interatomic potentials available for elemental germanium, with the scope to choose the potential suitable for the modeling of germanene (2D germanium) allotropes were investigated. Structural and mechanical…

Materials Science · Physics 2023-11-14 Marcin Maździarz

Screw dislocations in bcc metals display non-planar cores at zero temperature which result in high lattice friction and thermally activated strain rate behavior. In bcc W, electronic structure molecular statics calculations reveal a…

Machine-learned force fields have generated significant interest in recent years as a tool for molecular dynamics (MD) simulations, with the aim of developing accurate and efficient models that can replace classical interatomic potentials.…

Machine Learning · Computer Science 2023-04-05 Shaswat Mohanty , Sanghyuk Yoo , Keonwook Kang , Wei Cai

We propose a systematic method of analyzing pseudopotential transferability based on linear-response properties of the free atom, including self-consistent chemical hardness and polarizability. Our calculation of hardness extends the…

mtrl-th · Physics 2009-10-28 A. Filippetti , David Vanderbilt , W. Zhong , Yong Cai , G. B. Bachelet