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127 results for “monte carlo”
Data repository for "Scalable Quantum Monte Carlo with Direct-Product Trial Wave Functions"
<p>This Zenodo deposit provides a comprehensive collection of raw data, processed data, and scripts associated with the research paper available at <a href="https://arxiv.org/abs/2306.15186" target="_new">https://arxiv.org/abs/2306.15186</a>.</p> <p>Contents:</p> <p>- data.zip includes all the QMCpack inputs and outputs, which can be used in the blocking analysis to obtain the AFQMC energy. Processed Data "SI for DP-MSD-AFQMC.xlsx" contains the AFQMC results after the blocking analysis in XLSX format. "SI for DP-MSD-AFQMC.xlsx" also includes the energies from all other methods presented in the paper.</p> <p>- scripts.zip contains all the Python scripts used in running calculations for the paper. This includes scripts to generate LAS-AFQMC and CAS-AFQMC trials (under scripts/utils) in QMCpack format, as well as inputs for each compound.</p>
Dataset from "Monte Carlo-Discrete Dislocation Dynamics: A technique for studying the formation and evolution of dislocation structures"
<p>Raw datasets used in the manuscript:</p> <p>N. Deka and R. B. Sills, Monte Carlo-Discrete Dislocation Dynamics: A technique for studying the formation and evolution of dislocation structures, Modelling and Simulation in Materials Science and Engineering, 2022</p> <p>doi: 10.1088/1361-651X/ac41a2</p> <p>See Readme.txt file for details.</p>
Monte Carlo Validation Data
<p>The data required to perform the analysis outlined in <a href="https://github.com/teaghan/PET_MonteCarlo">this github repository</a>, which accompanies the paper (still to come...)</p>
Inelastic scattering of electrons in water from first principles: Cross sections and inelastic mean free path for use in Monte Carlo track-structure simulations of biological damage
<p>Modeling the inelastic scattering of electrons in water is fundamental, given their crucial role in biological damage. In Monte Carlo track-structure (MC-TS) codes used to assess biological damage, the energy loss function (ELF), from which cross sections are extracted, is derived from different semi-empirical optical models. Only recently have first <em>ab initio</em> results for the ELF and cross sections in water become available. For benchmarking purposes, in this work, we present <em>ab initio</em> linear-response time-dependent density functional theory calculations of the ELF of liquid water. We calculated the inelastic scattering cross sections, inelastic mean free paths, and electronic stopping power and compared our results with recent calculations and experimental data showing a good agreement. In addition, we provide an in-depth analysis of the contributions of different molecular orbitals, species, and orbital angular momenta to the total ELF. Moreover, we present single-differential cross sections computed for each molecular orbital channel, which should prove useful for MC-TS simulations.</p>
Monte Carlo data for "Melting of generalized Wigner crystals in transition metal dichalcogenide heterobilayer Moiré systems"
<p>Monte Carlo lattice configuration snapshots used to produce the results in the paper "Melting of generalized Wigner crystals in transition metal dichalcogenide heterobilayer Moiré systems." For further details and analysis scripts, see the associated Github repository at https://github.com/KimGroup/tmd_moire_monte_carlo.</p>
Monte Carlo simulation of the 2018 World Cup
<p>Generated using </p> <pre> </pre> <p><a href="https://doi.org/10.5281/zenodo.1284221">https://doi.org/10.5281/zenodo.1284221</a></p> <p> </p> <p> </p>
Research Data Supporting "Diagrammatic Coupled Cluster Monte Carlo"
<p>Research data supporting "Diagrammatic Coupled Cluster Monte Carlo". Includes all inputs and outputs for diagrammatic and unlinked Coupled Cluster Monte Carlo simulations on systems of noninteracting Neon and Beryllium replicas, and the separation of the Helium pentamer. This dataset also includes all code used to generate diagCCMC results and to analyse and plot all data. Unlinked CCMC calculations were performed using the open-source HANDE QMC package (http://www.hande.org.uk/).</p>
Determinant Quantum Monte Carlo data for the Hubbard model on the square lattice on a (t,U) grid.
<p>Data generated with QUEST 1.4.9. For documentation see these two homepages:<br> Original homepage: http://quest.ucdavis.edu/<br> Newest version available at: https://code.google.com/archive/p/quest-qmc/</p> <p>Available data from equal time measurements:</p> <ul> <li>up-up charge correlation function</li> <li>up-dn charge correlation function</li> <li>sz-sz spin correlation function</li> <li>pair correlation function</li> <li>greens function</li> <li>kinetic energy</li> <li>total energy</li> <li>chi thermal</li> <li>squared magnetization</li> <li>ZZ AF structure factor</li> </ul> <p>Data for the square lattice calculated for</p> <ul> <li>lattice sizes 8x8, 10x10, 12x12</li> <li>trotter discretizations 0.05, 0.1, 0.2</li> <li>inverse temperature 10.0</li> <li>U 0.0 to 2.7 in steps of 0.1</li> <li>t from 1.0 to 1.48 in steps of 0.02</li> </ul> <p>All simulations are done for half filling.</p> <p>The data are used in the publication "Thermodynamics of the metal-insulator transition in the extended Hubbard model" available on the arXiv (arXiv:1903.09947). There it is used to do an extrapolation of finite size and finite trotter errors and calculate the free energy by integrating the double occupation.</p> <p>The data are available in hdf5 archives and can easily be accessed, e.g., with python and h5py. An example python script is included. Relevant input parameters are included in the h5 files.</p> <p>All calculated quantities are averaged over multiple consecutive simulations, which is why the data is not presented in the usual QUEST output. This was necessary due to limited walltime on the used supercomputer.</p> <p>The authors acknowledge the North-German Supercomputing Alliance (HLRN) for providing computing resources via project number hbp00046 that have contributed to these results.</p>
Monte-Carlo simulations of aerosol-based exposure to SARS-CoV-2 in a shop/bar
<p>Data from new Monte-Carlo simulations of aerosol-based exposure to SARS-CoV-2 in shop and bar environments. For background and previous results see V. Vuorinen et al. Safety Science 130 (2020): 104866.</p> <p>The files are collected to a tar file that includes all data files used for analysis (binary files with NumPy data type .npy) and a readme.</p>
Neutronic analysis of the fusion reactor ARC: Monte Carlo simulations with the Serpent code
<p>The input files of the spatially-uniform and non-uniform neutron sources used for the Monte Carlo simulations with the Serpent code and the CAD files for the geometry definition of the fusion reactor ARC used in the framework of the work published in the paper "Neutronic analysis of the fusion reactor ARC: Monte Carlo simulations with the Serpent code" (in Fusion Science and Technology). In the repository there are also the Python files used for the post processing of the results and the output files of Serpent with the main results of the neutronic simulations.</p>
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</p>
Datasets for numerical Monte Carlo simulations of Diclofenac bio-degradation in a soil-water system
<p>This record contains files and main instructions to repeat the numerical simulations of Diclofenac bio-degradation is a soil-water system. The text file Readme.docx contains a description of the overall content of this record, which is organized in two separate folders.</p>
Data files for "A hybrid Monte Carlo study of bond-stretching electron-phonon interactions and charge order in the bismuthate family of superconductors"
<p>This data set contains the data and scripts for the paper "A hybrid Monte Carlo study of bond-stretching electron-phonon interactions and charge order in the bismuthate family of superconductors" by Benjamin Cohen-Stead, Kipton Barros, Richard Scalettar, and Steven Johnston. </p> <p>Preprint: https://arxiv.org/pdf/2208.02339.pdf</p>
Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network
<p>This repository contains:</p> <ul> <li>openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data.</li> <li>out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.</li> </ul>
Data for "Monte Carlo samplers for efficient network inference"
<p>This directory contains data corresponding to all figures in the publication "Monte Carlo samplers for efficient network inference" in PLOS Computational Biology by Z. Kilic et al.</p>
Monte Carlo Arithmetic Instrumented DeepGOPlus Protein Function Predictions
<p>This dataset contains the perturbed protein function predictions by the DeepGOPlus model excluding the Diamond tool component. The model was perturbed with Verrou, an implementation of Monte Carlo Arithmetic (MCA), a stochastic arithmetic technique that injects noise into a program that simulates changes in a user's execution environment. The folders contain pkl files that can be read with the Pandas python library to load up dataframes containing the predictions and original values. Each file is one MCA sample run across the entire DeepGOPlus test set.</p> <p>The folder "Verrou_All" contains predictions where the entirety of the model was instrumented with MCA.</p> <p>The folder "Verrou_TF" contains predictions where only the Tensorflow library was instrumented with MCA.</p> <p>The folder "Fuzzy_Python" contains predictions where only the Python interpreter was instrumented with MCA.</p> <p>The folder "VPREC_Outbound_Mode" contains predictions where the virtual precision of the floating point operations was reduced witht the VPREC precision simulator tool in outbound mode.</p> <p>The folder "VPREC_Inbound_Mode" contains predictions where the virtual precision of the floating point operations was reduced witht the VPREC precision simulator tool in inbound mode.</p> <p>More information can be found by consulting <a href="https://arxiv.org/abs/2212.06361">this paper</a> or <a href="https://github.com/big-data-lab-team/deepgoplus-stability">this Github repository</a>.</p>
A Pilot Study to Investigate the Clinical Utility of the Peregrine Monte Carlo Dose Calculation System for Radiation Therapy Treatment Planning
ClinicalTrials.gov study NCT00026936. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Bayesian adaptive Markov Chain Monte Carlo estimation of genetic parameters
Open the record for dataset details and reuse information.
Inelastic scattering of electrons in water from first principles: Cross sections and inelastic mean free path for use in Monte Carlo track-structure simulations of biological damage
Open the record for dataset details and reuse information.
Monte-Carlo Simulation Models of Animal Movement
This dataset consists of a series of computer programs in the PASCAL language that implement a simple model of animal movement. Results of model use were published in: Porter, J.H. and J.L. Dooley, Jr. 1993. Animal dispersal patterns: a reassessment of simple mathematical models. Ecology 74:2436-2443. MOVESIM.PAS, MOVESV1.PAS and MOVESV21.PAS are the model. Input is a set of coordinates for sampling points. The model randomly selects a starting point, then selects a random distance and direction. The distance to the nearest sampling point is then calculated and the process is repeated. There are a number of associated programs. MOVESTAT.SAS - SAS program to list frequency histogram for MOVESIM output. RANDGEN.PAS - Generate a set of random sampling stations within a given area. FITIT.PAS - Estimate fit of different movement models to output. ADJUST1.PAS - Estimate the number of individuals that would have been caught at a given distance if sample points had been evenly distributed.
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