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Diamond is studied by path integral molecular dynamics simulations of the atomic nuclei in combination with a tight-binding Hamiltonian to describe its electronic structure and total energy. This approach allows us to quantify the influence…

Materials Science · Physics 2009-11-11 Rafael Ramirez , Carlos P. Herrero , Eduardo R. Hernandez

Atomistic theory holds the promise for the ab initio development of superalloys based on the fundamental principles of quantum mechanics. The last years showed a rapid progress in the field. Results from atomistic modeling enter…

Materials Science · Physics 2021-11-08 Thomas Hammerschmidt , Jutta Rogal , Erik Bitzek , Ralf Drautz

Controlling polymorphism in molecular crystals is crucial in the pharmaceutical, dye, and pesticide industries. However, its theoretical description is extremely challenging, due to the associated long timescales ($ > 1 \, \mu s$). We…

Chemical Physics · Physics 2023-02-09 Oren Elishav , Roy Podgaetsky , Olga Meikler , Barak Hirshberg

The ultra-long relaxation time of glass transition makes it difficult to construct atomic models of amorphous materials by conventional methods. We propose a novel method for building such atomic models using data assimilation method by…

Materials Science · Physics 2022-12-14 Yuansheng Zhao , Ryuhei Sato , Shinji Tsuneyuki

We propose a method to simulate spin models in trapped ions using a digital-analog approach, consisting in a suitable gate decomposition in terms of analog blocks and digital steps. In this way, we show that the quantum dynamics of an…

Quantum Physics · Physics 2016-08-01 I. Arrazola , J. S. Pedernales , L. Lamata , E. Solano

We develop a rigorous framework for modelling the geometry equilibration of crystalline defects. We formulate the equilibration of crystal defects as a variational problems on a discrete energy space and establish qualitatively sharp…

Numerical Analysis · Mathematics 2018-10-16 Huajie Chen , Faizan Q. Nazar , Christoph Ortner

Here, we propose a comprehensive first-principle atomistic approach to predict the Wulff-Kaischew equilibrium shape of crystals heterogeneously integrated on a dissimilar material. This method uses both reconstructed surface and interface…

A four-way coupling scheme for the direct numerical simulation of particle-laden flows is developed and analyzed. It employs a novel adaptive multi-relaxation time lattice Boltzmann method to simulate the fluid phase efficiently. The…

Computational Physics · Physics 2020-03-04 Christoph Rettinger , Ulrich Rüde

Simulations that couple different classical molecular models in an adaptive way by changing the number of degrees of freedom on the fly, are available within reasonably consistent theoretical frameworks. The same does not occur when it…

Soft Condensed Matter · Physics 2015-05-18 A. B. Poma , L. Delle Site

Differentiable physics simulation provides an avenue to tackle previously intractable challenges through gradient-based optimization, thereby greatly improving the efficiency of solving robotics-related problems. To apply differentiable…

Robotics · Computer Science 2025-04-08 Min Liu , Gang Yang , Siyuan Luo , Lin Shao

A long-standing goal of materials science is to understand, predict and control the evolution of microstructures in crystalline materials. Most microstructure evolution is controlled by interface motion; hence, the establishment of rigorous…

Materials Science · Physics 2022-02-14 Jian Han , David J. Srolovitz , Marco Salvalaglio

A continuum theory is used to predict scaling laws for the morphological relaxation of crystal surfaces in two independent space dimensions. The goal is to unify previously disconnected experimental observations of decaying surface…

Materials Science · Physics 2009-11-13 Dionisios Margetis

Asymptotics-based configuration-interaction (CI) methods [G. Friesecke and B. D. Goddard, Multiscale Model. Simul. 7, 1876 (2009)] are a class of CI methods for atoms which reproduce, at fixed finite subspace dimension, the exact…

Quantum Physics · Physics 2012-04-06 Christian Mendl , Gero Friesecke

We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains…

Chemical Physics · Physics 2023-11-08 Yuchao Lin , Keqiang Yan , Youzhi Luo , Yi Liu , Xiaoning Qian , Shuiwang Ji

Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these…

Machine Learning · Computer Science 2024-05-08 Jonathan Balasingham , Viktor Zamaraev , Vitaliy Kurlin

We developed a new physical model to predict macroscopic properties of inorganic molten systems using a realistic description of inter-atomic interactions. Unlike the conventional approach, which tends to overestimate viscosity by several…

Materials Science · Physics 2015-06-04 Vitaly V. Chaban , Yuriy V. Pereverzev , Oleg V. Prezhdo

A simple and general formalism for mode coupling by a spatial, temporal or spatiotemporal perturbation in dispersive materials is developed. This formalism can be used for studying various linear and non-linear optical interactions…

Optics · Physics 2015-06-19 Brenda Dana , Lilya Lobachinsky , Alon Bahabad

A finite-deformation crystal-elasticity membrane model for Transition Metal Dichalcogenide (TMD) monolayers is presented. Monolayer TMDs are multi-atom-thick two-dimensional (2D) crystalline membranes having atoms arranged in three parallel…

Materials Science · Physics 2022-09-14 Upendra Yadav , Susanta Ghosh

We have investigated plasma-surface interactions with molecular dynamics (MD) simulations. It, however, is high cost computation and is limited to simulations for materials of nanometer order. In order to overcome the limitation, a…

Materials Science · Physics 2010-05-20 Arimichi Takayama , Seiki Saito , Atsushi M. Ito , Takahiro Kenmotsu , Hiroaki Nakamura

Deep learning-based generative models have emerged as powerful tools for modeling complex data distributions and generating high-fidelity samples, offering a transformative approach to efficiently explore the configuration space of…

Materials Science · Physics 2025-11-12 Xiaoshan Luo , Zhenyu Wang , Qingchang Wang , Jian Lv , Lei Wang , Yanchao Wang , Yanming Ma