Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

The nucleation of crystals in liquids is one of nature's most ubiquitous phenomena, playing an important role in areas such as climate change and the production of drugs. As the early stages of nucleation involve exceedingly small time and length scales, atomistic computer simulations can provide unique insight into the microscopic aspects of crystallization. In this review, we take stock of the numerous molecular dynamics simulations that in the last few decades have unraveled crucial aspects of crystal nucleation in liquids. We put into context the theoretical framework of classical nucleation theory and the state of the art computational methods, by reviewing simulations of e.g. ice nucleation or crystallization of molecules in solutions. We shall see that molecular dynamics simulations have provided key insight into diverse nucleation scenarios, ranging from colloidal particles to natural gas hydrates, and that in doing so the general applicability of classical nucleation theory has been repeatedly called into question. We have attempted to identify the most pressing open questions in the field. We believe that by improving (i.) existing interatomic potentials; and (ii.) currently available enhanced sampling methods, the community can move towards accurate investigations of realistic systems of practical interest, thus bringing simulations a step closer to experiments.

Crystal Nucleation in Liquids: Open Questions and Future Challenges in Molecular Dynamics Simulations

The liquid state is one of the fundamental and essential states of matter, but its physical understanding is far behind the other states, such as the gas and solid states, due to the difficulties associated with the high density causing many-body correlations and the lack of long-range order. Significant open problems in liquid science include glass transition, crystallization, and water's anomalies. Austen Angell has contributed tremendously to these problems and proposed many new concepts of fundamental importance. In this article, we review how these concepts have influenced our work on liquid physics, focusing on the roles of liquid structural ordering in glass transition, crystallization, and water's anomalies. 

Roles of liquid structural ordering in glass transition, crystallization, and water's anomalies

Abstract In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphization requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and the liquid states. Taking into account rare nucleation events or structural relaxation under deep undercooling conditions requires much larger length scales and longer time scales than those achievable by ab initio molecular dynamics (AIMD). This problem is addressed by means of classical molecular dynamics simulations using a well established high dimensional neural network potential trained on a set of configurations generated by AIMD relevant for solidification phenomena. Our dataset contains various crystalline structures and liquid states at different pressures, including their time fluctuations in a wide range of temperatures. Applied to elemental aluminium, the resulting potential is shown to be efficient to reproduce the basic structural, dynamics and thermodynamic quantities in the liquid and undercooled states. Early stages of crystallization are further investigated on a much larger scale with one million atoms, allowing us to unravel features of the homogeneous nucleation mechanisms in the fcc phase at ambient pressure as well as in the bcc phase at high pressure with unprecedented accuracy close to the ab initio one. In both cases, a single step nucleation process is observed. 

Machine learning interatomic potentials for aluminium: application to solidification phenomena

/pdf/transferability-of-zr-zr-interatomic-potentials-3n46z9sy.pdf

Transferability of Zr-Zr interatomic potentials

We report large-scale molecular dynamics simulations of the glass formation from the liquid phase and homogeneous nucleation phenomena of pure zirconium. For this purpose, we have built a modified embedded atom model potential in order to reproduce relevant structural, dynamic, and thermodynamic properties from ab initio and experimental data near the melting point. By means of liquid-solid interface simulations, we show that this potential provides a thermodynamic melting temperature and densities of the solid and liquid state in good agreement with experiments. Using melt-quenching simulations with one million atoms, we determine the glass transition from the temperature evolution of the inherent structure energy as well as the nose of the time-temperature-transformation curve located in the deep undercooling regime. We identify the local structural origin of the glass-forming ability as a competition between bcc and fivefold polytetrahedral structures that may represent an impediment of rapid homogeneous nucleation at such high undercoolings. This suggests the ability of single elemental zirconium to form a glass from the melt with cooling rates of at least ${10}^{12}\phantom{\rule{4pt}{0ex}}\mathrm{K}/\mathrm{s}$, compatible with modern experiments.

/pdf/glass-forming-ability-of-elemental-zirconium-4inxqxrntf.pdf

Glass-forming ability of elemental zirconium

Nucleation phenomena commonly observed in our every day life are of fundamental, technological and societal importance in many areas, but some of their most intimate mechanisms remain however to be unravelled. Crystal nucleation, the early stages where the liquid-to-solid transition occurs upon undercooling, initiates at the atomic level on nanometre length and sub-picoseconds time scales and involves complex multidimensional mechanisms with local symmetry breaking that can hardly be observed experimentally in the very details. To reveal their structural features in simulations without a priori, an unsupervised learning approach founded on topological descriptors loaned from persistent homology concepts is proposed. Applied here to monatomic metals, it shows that both translational and orientational ordering always come into play simultaneously when homogeneous nucleation starts in regions with low five-fold symmetry. It also reveals the specificity of the nucleation pathways depending on the element considered, with features beyond the hypothesis of Classical Nucleation Theory.

Unsupervised topological learning approach of crystal nucleation

In these notes we give a version of the Alperin–Goldschmidt fusion theorem for localities.

Alperin's Fusion Theorem for localities

Cohomology with twisted coefficients of the classifying space of a fusion system

https://hal.archives-ouvertes.fr/hal-01679071/document

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Rémi Molinier

Author Tools

Chat about Author

Papers

Glass-forming ability of elemental zirconium

Unsupervised topological learning approach of crystal nucleation

Alperin's Fusion Theorem for localities

Cohomology with twisted coefficients of the classifying space of a fusion system

Alperin's Fusion Theorem for localities