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Machine Learning Interatomic Potentials (MLIPs) are a modern computational method that allows achieving near-quantum mechanical accuracy (DFT) while still describing large-scale systems in molecular dynamics (MD) simulations. In this work,…

Materials Science · Physics 2026-02-13 Le Huu Nghia , Pham Thi Bich Thao , Truong Do Anh Kha , Vo Khuong Dien , Nguyen Thanh Tien

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' as they can be mixed to precisely tailor the physiochemical…

We present a physically motivated strategy for the construction of training sets for transferable machine learning interatomic potentials. It is based on a systematic exploration of all possible space groups in random crystal structures,…

Materials Science · Physics 2023-03-29 Marvin Poul , Liam Huber , Erik Bitzek , Jörg Neugebauer

We study a generalization performance of the machine learning (ML) model to predict the atomic forces within the density functional theory (DFT). The targets are the Si and Ge single component systems in the liquid state. To train the…

Computational Physics · Physics 2019-03-27 Ryo Tamura , Jianbo Lin , Tsuyoshi Miyazaki

Ni/Al reactive multilayers are promising materials for applications requiring controlled local energy release and superior mechanical performance. This study systematically investigates the impact of compositional variations, ranging from…

Materials Science · Physics 2026-04-14 Nensi Toncich , Fabian Schwarz , Rebecca A. Gallivan , Jemma Gillon , Ralph Spolenak

Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often difficult to determine experimentally. Molecular dynamics can…

Computational Physics · Physics 2026-03-26 Ian Störmer , Julija Zavadlav

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

ANN (Artificial Neural Networks) modeling methodology was adopted for predicting mechanical properties of aluminum cast composite materials. For this purpose aluminum alloy were developed using conventional foundry method. The composite…

Materials Science · Physics 2016-06-01 Muhammad Hayat Jokhio , Muhammad Ibrahim Panhwer , Mukhtiar Ali Unar

Metals which can form intermetallic compounds by an exothermic reaction constitute a class of reactive materials with multiple applications. Ni-Al laminates of thin alternating layers are being considered as model nanometric metallic…

Materials Science · Physics 2015-06-17 Luis Sandoval , Geoffrey H. Campbell , Jaime Marian

The high-temperature properties of the Al(111) surface are studied by molecular-dynamics simulation. This surface does not melt below the bulk melting point, but can be superheated. Superheating of metal surfaces has been recently observed…

Materials Science · Physics 2009-10-30 G. Bilalbegovic

Atomic scale simulations at finite temperature are an ideal approach to study the thermodynamic properties of magnetic transition metals. However, the development of interatomic potentials explicitly taking into account magnetic variables…

Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of…

Chemical Physics · Physics 2026-02-24 Valerii Andreichev , Jindra Dušek , Markus Meuwly , Jeremy O. Richardson

First-principles based modeling on phonon dynamics and transport using density functional theory and Boltzmann transport equation has proven powerful in predicting thermal conductivity of crystalline materials, but it remains unfeasible for…

Materials Science · Physics 2019-07-23 Xin Qian , Shenyou Peng , Xiaobo Li , Yujie Wei , Ronggui Yang

Alloys present the great potential in catalysis because of their adjustable compositions, structures and element distributions, which unfortunately also limit the fast screening of the potential alloy catalysts. Machine learning methods are…

Materials Science · Physics 2021-07-07 Xin Li , Bo Li , Zhiwen Chen , Wang Gao , Qing Jiang

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in…

We apply a recently developed optimization scheme to obtain effective potentials for alkali and alkaline-earth aluminosilicate glasses that contains lithium, sodium, potassium, or calcium as modifiers. As input data for the optimization, we…

Computational Physics · Physics 2019-05-22 Siddharth Sundararaman , Liping Huang , Simona Ispas , Walter Kob

We report a study on the static and dynamic properties of several liquid 5$d$ transition metals at thermodynamic conditions near their respective melting points. This is performed by resorting to ab initio molecular dynamics simulations in…

Statistical Mechanics · Physics 2023-08-15 D. J. González , L. E. González

In this study some properties of the methanol-water mixture such as diffusivity, density, viscosity, and hydrogen bonding were calculated at different temperatures and atmospheric pressure using molecular dynamics simulations (MDS). The…

Atomic and Molecular Clusters · Physics 2017-08-08 Palazzo Mancini , Mara Cantoni

The influence of pre-strain and temperature on the superior properties exhibited by an Nb nanowire embedded in a NiTi shape memory alloy (SMA) are investigated via molecular dynamics simulations. To this end, a new Nb-Ni-Ti ternary…

Materials Science · Physics 2022-03-02 Jung Soo Lee , Won-Seok Ko , Blazej Grabowski

Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is…

Materials Science · Physics 2022-01-13 Giulio imbalzano , Michele Ceriotti
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