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It is well established that physical aging of amorphous solids is governed by a marked change in dynamical properties as the material becomes older. Conversely, structural properties such as the radial distribution function exhibit only a…

We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based…

Chemical Physics · Physics 2019-04-17 Lixue Cheng , Matthew Welborn , Anders S. Christensen , Thomas F. Miller

Unraveling the structural factors influencing the dynamics of amorphous solids is crucial. While deep learning aids in navigating these complexities, transparency issues persist. Inspired by the successful application of prototype neural…

Soft Condensed Matter · Physics 2024-03-19 Xiao Jiang , Zean Tian , Kenli Li , Wangyu Hu

Describing the interactions of water molecules is one of the most common, yet critical, tasks in molecular dynamics simulations. Because of its unique properties, hundreds of attempts have been made to construct an ideal interaction…

Chemical Physics · Physics 2025-06-23 Bálint Soczó , Ildikó Pethes

The influence of thermal processing on the potential energy, atomic structure, and mechanical properties of metallic glasses is examined using molecular dynamics simulations. We study the three-dimensional binary mixture, which was first…

Soft Condensed Matter · Physics 2019-12-23 Nikolai V. Priezjev

We study theoretically and numerically a family of multi-point dynamic susceptibilities that quantify the strength and characteristic lengthscales of dynamic heterogeneities in glass-forming materials. We use general theoretical arguments…

Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time scale and complexity. Given the interpolative nature of…

We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input.The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular…

Chemical Physics · Physics 2018-10-16 Matthew Welborn , Lixue Cheng , Thomas F. Miller

A transferable tight-binding potential has been constructed for heteroatomic systems containing carbon and hydrogen. The electronic degree of freedom is treated explicitly in this potential using a small set of transferable parameters which…

chem-ph · Physics 2008-02-03 Yang Wang , C. H. Mak

The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an…

Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable datasets to ensure robust…

Machine Learning · Computer Science 2025-02-20 Sebastien Röcken , Julija Zavadlav

Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is…

The evolution of porous structure, potential energy and local density in binary glasses under oscillatory shear deformation is investigated using molecular dynamics simulations. The porous glasses were initially prepared via a rapid thermal…

Soft Condensed Matter · Physics 2022-10-25 Nikolai V. Priezjev , Maxim A. Makeev

Little is known about the coupling of rotation and translation in dense systems. Here, we report results of confocal fluorescence microscopy where simultaneous recording of translational and rotational particle trajectories from a…

Soft Condensed Matter · Physics 2024-11-08 John Geiger , Niklas Grimm , Matthis Fuchs , Andreas Zumbusch

Density functional theory offers a very accurate way of computing materials properties from first principles. However, it is too expensive for modelling large-scale molecular systems whose properties are, in contrast, computed using…

Computational Physics · Physics 2016-12-12 Alexander V. Shapeev

We have studied the dependence of metal oxide properties in molecular dynamics (MD) simulations on the polarizability of oxygen ions. We present studies of both liquid and crystalline structures of silica (SiO2), magnesia (MgO) and alumina…

Materials Science · Physics 2012-11-13 Philipp Beck , Peter Brommer , Johannes Roth , Hans-Rainer Trebin

We review the recent literature on the simulation of the structure and deformation of amorphous glasses, including oxide and metallic glasses. We consider simulations at different length and time scales. At the nanometer scale, we review…

Mesoscale and Nanoscale Physics · Physics 2015-05-28 David Rodney , Anne Tanguy , Damien Vandembroucq

Computational modeling is usually applied to aid experimental exploration of advanced materials to better understand the fundamental plasticity mechanisms during mechanical testing. In this work, we perform Molecular dynamics (MD)…

Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable both the elucidation of fundamental mechanisms and the engineering of matter for desired tasks. The behavior of molecular systems at the microscale is…

Computational Physics · Physics 2020-12-25 Wujie Wang , Simon Axelrod , Rafael Gómez-Bombarelli

The reformulation of the mode-coupling theory (MCT) of the liquid-glass transition which incorporates the element of metastability is applied to the hard-sphere system. It is shown that the glass transition in this system is not a sharp one…

Condensed Matter · Physics 2016-08-31 Joonhyun Yeo
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