English
Related papers

Related papers: Developing atomistic glass models using potential-…

200 papers

We introduce a `virtual-move' Monte Carlo (VMMC) algorithm for systems of pairwise-interacting particles. This algorithm facilitates the simulation of particles possessing attractions of short range and arbitrary strength and geometry, an…

Statistical Mechanics · Physics 2009-11-11 Stephen Whitelam , Phillip L. Geissler

Due to the complex characteristics of bottle-brush polymers, it became a challenge to develop an efficient algorithm for studying such macromolecules under various solvent conditions or some constraints in the space by using computer…

Soft Condensed Matter · Physics 2011-07-08 Hsiao-Ping Hsu

Dynamic Programming (DP) suffers from the well-known ``curse of dimensionality'', further exacerbated by the need to compute expectations over process noise in stochastic models. This paper presents a Monte Carlo-based sampling approach for…

Systems and Control · Electrical Eng. & Systems 2024-09-10 Mohammad S. Ramadan , Ahmad Al-Tawaha , Mohamed Shouman , Ahmed Atallah , Ming Jin

In simulations of crystals, unlike liquids or gases, it may happen that the properties of the studied system depend not only on the volume of the simulation cell but also on its shape. For such cases it is desirable to change the shape of…

Computational Physics · Physics 2022-10-04 A. Baumketner

Bulk metallic glasses (BMGs) are amorphous alloys with desirable mechanical properties and processing capabilities. To date, the design of new BMGs has largely employed empirical rules and trial-and-error experimental approaches. Ab initio…

Materials Science · Physics 2015-06-23 Kai Zhang , Yanhui Liu , Jan Schroers , Mark D. Shattuck , Corey S. O'Hern

We present a model and protocol that enable the generation of extremely stable computer glasses at minimal computational cost. The protocol consists of an instantaneous quench in an augmented potential energy landscape, with particle radii…

Soft Condensed Matter · Physics 2019-01-08 Geert Kapteijns , Wencheng Ji , Carolina Brito , Matthieu Wyart , Edan Lerner

Topological constraint theory has become an increasingly popular tool to predict the compositional dependence of glass properties or pinpoint promising compositions with tailored functionalities. This approach reduces complex disordered…

Disordered Systems and Neural Networks · Physics 2020-06-16 Mathieu Bauchy

The structural motifs of a Zr$_{50}$Cu$_{45}$Al$_{5}$ metallic glass were learned from atomistic models using a new structure analysis method called motif extraction that employs point-pattern matching and machine learning clustering…

Materials Science · Physics 2019-07-19 Jason J. Maldonis , Arash Dehghan Banadaki , Srikanth Patala , Paul M. Voyles

We propose a new method to construct confidence intervals for quantities that are associated with a stationary time series, which avoids direct estimation of the asymptotic variances. Unlike the existing tuning-parameter-dependent…

Methodology · Statistics 2010-05-13 Xiaofeng Shao

We develop a generic strategy and simple numerical models for multi-component metallic glasses for which the swap Monte Carlo algorithm can produce highly stable equilibrium configurations equivalent to experimental systems cooled more than…

Statistical Mechanics · Physics 2020-10-09 Anshul D. S. Parmar , Misaki Ozawa , Ludovic Berthier

We present a Markov Chain Monte Carlo algorithm based on the Metropolis algorithm for simulation of the flow of two immiscible fluids in a porous medium under macroscopic steady-state conditions using a dynamical pore network model that…

Fluid Dynamics · Physics 2016-12-20 Isha Savani , Santanu Sinha , Alex Hansen , Dick Bedeaux , Signe Kjelstrup , Morten Vassvik

We discuss the rejection-free event-chain Monte-Carlo algorithm and several applications to dense soft matter systems. Event-chain Monte-Carlo is an alternative to standard local Markov-chain Monte-Carlo schemes, which are based on detailed…

Soft Condensed Matter · Physics 2021-04-29 Tobias A. Kampmann , David Müller , Clemens Franz Vorsmann , Lukas Paul Weise , Jan Kierfeld

In this paper we address the problem of the uniform random generation of non deterministic automata (NFA) up to isomorphism. First, we show how to use a Monte-Carlo approach to uniformly sample a NFA. Secondly, we show how to use the…

Formal Languages and Automata Theory · Computer Science 2015-12-10 Pierre-Cyrille Heam , Jean-Luc Joly

In this paper, we suggest a novel sampling method for Monte Carlo molecular simulations. In order to perform efficient sampling of molecular systems, it is advantageous to avoid extremely high energy configurations while also retaining the…

Computational Physics · Physics 2019-07-18 Katsuhiro Endo , Daisuke Yuhara , Kenji Yasuoka

We study propagating mode-I fracture in two dimensional amorphous materials using atomistic simulations. We used the continuous random network (CRN) model of an amorphous material, creating samples using a two dimensional analogue of the…

Materials Science · Physics 2015-05-20 Shay I Heizler , David A. Kessler , Herbert Levine

Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new,…

In this paper we describe a method to generate amorphous structures with arbitrary structural constraints. This method employs the Simulated Annealing algorithm to minimize a simple yet carefully tailored Cost Function (CF). The Cost…

Materials Science · Physics 2010-09-14 F. H. Jornada , V. Gava , A. L. Martinotto , L. A. Cassol , C. A. Perottoni

We describe a random matrix approach that can provide generic and readily soluble mean-field descriptions of the phase diagram for a variety of systems ranging from QCD to high-T_c materials. Instead of working from specific models, phase…

High Energy Physics - Phenomenology · Physics 2015-05-28 Benoit Vanderheyden , A D Jackson

Glass transitions are widely observed in various types of soft matter systems. However, the physical mechanism of these transitions remains {elusive}, despite years of ambitious research. In particular, an important unanswered question is…

Disordered Systems and Neural Networks · Physics 2022-12-20 Norihiro Oyama , Shihori Koyama , Takeshi Kawasaki

Hybrid Monte Carlo (HMC) generates samples from a prescribed probability distribution in a configuration space by simulating Hamiltonian dynamics, followed by the Metropolis (-Hastings) acceptance/rejection step. Compressible HMC (CHMC)…

Computational Physics · Physics 2016-04-05 Akihiko Nishimura , David Dunson