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53 results for “machine learning potential”
Supporting Data for "Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"
<p>Supporting Data for "Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins"</p>
Linear machine learning based force matching for amorphous silica: How close are the classical two-body potentials to ab initio calculations?
<p>Please later see our manuscript (in submission) for details.</p>
Capturing the interactions in the BaSnF$_4$ ionic conductor: a comparison of machine-learning potentials and polarizable force fields
<p>The current files contain all the original data for our work "Capturing the interactions in the BaSnF<sub>4 </sub>ionic conductor: a comparison of machine-learning potentials and polarizable force fields". </p>
Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data
<p>This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling. This database is associated with the article published in the Revista Brasileira de Cartografia (RBC), entitled: Spatial Modeling of Groundwater Potential in the North of Minas Gerais, Brazil: An Integrated Approach Using Machine Learning and Environmental Data. This database contains the Groundwater flow rasters and the covariates used in spatial modeling.</p>
Modelling the potential distribution of African Wormwood (Artemisia afra) using machine learning algorithm-based approach (MaxEnt) in Sekhukhune District, South Africa
Open the record for dataset details and reuse information.
Data and scripts for 'Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes'
<p>Data and scripts for "Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes" by Axel Seifert and Stephan Rasp, J. Adv. Modeling Earth Systems, 12, 2020, https://doi.org/10.1029/2020MS002301</p>
Potential and training data for 'Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential'
<p>Fitted potential, training and testing data.</p>
Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites
<p>Data for reproducibility of the paper "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites"</p> <p>Corresponding paper:</p> <p>ShanPing Liu, Romain Dupuis, Dong Fan, Salma Benzaria, Mickaele Bonneau, Prashant Bhatt, Mohamed Eddaoudi, and Guillaume Maurin. "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites." <em>Chemical Science</em> (2024). https://doi.org/10.1039/D3SC05612K</p>
Research data supporting 'Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1−xWxS2−2ySe2y Alloys'
<p>Research data supporting 'Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1−xWxS2−2ySe2y Alloys'</p>
Supplemental data for "Benchmarking machine learning interatomic potentials via phonon anharmonicity"
<p>Supplementary data including all training data, machine learning models and irreducible derivatives used in the study.</p>
Machine Learning Potentials for Metal-Organic Frameworks with Thermodynamic Transferability: training data
<p>This dataset contains potential energies, forces, and virial stress for a large set of reference configurations for UiO-66(Zr) and MIL-53(Al), computed at the PBE-D3 level using CP2K 7.1. The basis set contained both TZVP Gaussian basis functions as well as plane waves (cutoff energy 800 Ry for UiO-66(Zr) and 900 Ry for MIL-53(Al)). The sampling of the Brillouin zone was restricted to the gamma point.</p>
Research data for "A machine-learned interatomic potential for silica and its relation to empirical models"
<p>This dataset supports the paper "A machine-learned interatomic potential for silica and its relation to empirical models". The paper is online here:</p> <p>The following files are provided:</p> <ul> <li>xyz-file containing all structures in the training database including forces and energies</li> <li>GAP file containing the corresponding parameters, which can be used for example for Lammps MD simulations</li> <li>Amorphous structure files for silica created by different interatomic potentials.</li> </ul>
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
<p>Supplementary data for the paper "GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations" by the GPUMD developers.</p>
Data for: Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
<p>This is the dataset for the article "Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials" by Juno Nam and Rafael Gomez-Bombarelli.</p> <ul> <li>Preprint: <a href="https://arxiv.org/abs/2404.10746">https://arxiv.org/abs/2404.10746</a></li> <li>Code: <a href="https://github.com/learningmatter-mit/alchemical-mlip">https://github.com/learningmatter-mit/alchemical-mlip</a></li> </ul>
Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning
<p>It contains reference training and test set configurations (and corresponding energy, forces, and virial stress values) for ZIF-8 and MOF-5.</p>
Experimental and Computational Study Towards Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials
<p>SnOx has received great attention as an electrocatalyst for CO2 reduction reaction (CO2RR), however, it still suffers from low activity. Moreover, the atomic-level SnOx structure and the nature of the active sites are still ambiguous due to the dynamism of surface structure and difficulty in structure characterization under electrochemical conditions. Herein, we first enhance its CO2RR performance by supporting SnO2 nanoparticles on two common supports, Vulcan Carbon and TiO2 . Then, electrolysis of CO2 at various temperatures in a neutral electrolyte reveals that the application window for this catalyst is between 12 and 30 °C.<br>Furthermore, our study introduces a machine learning interatomic potential method for the atomistic simulation to investigate SnO 2 reduction and establish a correlation between SnO x structures and their CO 2 RR performance. In addition, selectivity is analyzed computationally with density functional theory simulations to identify the key differences between the binding energies of *H and *CO2−, where both are correlated with the presence of oxygen on the nanoparticle surface. This study offers in-depth insights into the rational design and application of SnOx -based electrocatalysts for CO2RR.</p>
Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks
<p>Scripts and data for the publication</p>
Data for the manuscript "Spatially resolved uncertainties for machine learning potentials"
<p>This repository accompanies the manuscript "Spatially resolved uncertainties for machine learning potentials" by E. Heid, J. Schörghuber, R. Wanzenböck, and G. K. H. Madsen. The following files are available:</p> <ul> <li> <p><code>mc_experiment.ipynb</code> is a Jupyter notebook for the Monte Carlo experiment described in the study (artificial model with only variance as error source).</p> </li> </ul> <ul> <li> <p><code>aggregate_cut_relax.py</code> contains code to cut and relax boxes for the water active learning cycle.</p> </li> <li> <p><code>data_t1x.tar.gz</code> contains reaction pathways for 10,073 reactions from a subset of the Transition1x dataset, split into training, validation and test sets. The training and validation sets contain the indices 1, 2, 9, and 10 from a 10-image nudged-elastic band search (40k datapoints), while the test set contains indices 3-8 (60k datapoints). The test set is ordered according to the reaction and index, i.e. rxn1_index3, rxn1_index4, [...] rxn1_index8, rxn2_index3, [...].</p> </li> <li> <p><code>data_sto.tar.gz</code> contains surface reconstructions of SrTiO3, randomly split into a training and validation set, as well as a test set.</p> </li> <li> <p><code>data_h2o.tar.gz</code> contains:</p> <ul> <li> <p><code>full_db.extxyz</code>: The full dataset of 1.5k structures.</p> </li> <li> <p><code>iter00_train.extxyz</code> and <code>iter00_validation.extxyz</code>: The initial training and validation set for the active learning cycle.</p> </li> <li> <p>the subfolders in the folders <code>random</code>, and <code>uncertain</code>, and <code>atomic</code> contain the training and validation sets for the random and uncertainty-based (local or atomic) active learning loops.</p> </li> </ul> </li> </ul>
DFT datasets for training machine-learning potential to model Cl-doped lithium borosilicate glasses using DeePMD
<h2><strong>Li diffusion in oxygen-chlorine mixed anion borosilicate glasses using</strong></h2> <h2><strong>a machine-learning simulation</strong></h2> <h5>Shingo Urata, Noriyoshi Kayaba</h5> <ul> <li>DFT_Data_for_Cl-doped_LBSCl_glass.zip inlucudes atom configurations, energies, forces, box size, atom types, atom kinds, and virial in coord.raw, energy.raw, force.raw, type.raw, type_map.raw, and virial.raw, respectively. </li> <li>All DFT data were evaluated using PBE with a cutoff energy of 600 eV by VASP.</li> <li>The other detasets are available from https://doi.org/10.5281/zenodo.10577559</li> <li>LBSCl_DMD_model.pb is force field developed using DeePMD-kit.</li> <li>LBSCl_DMD_model_c.pb.zip is the compressed version of LBSCl_DMD_model.pb.</li> </ul>
Supplementary initial and final MD configurations for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>Extended XYZ files for the initial and final configurations of the molecular dynamics simulations from the article 'General-purpose machine-learned potential for 16 elemental metals and their alloys' (https://arxiv.org/abs/2311.04732).</p> <p><span>The Supplementary Data is contained in the folder named:<br>1) Polycrystalline-MoTaVW: <span> </span>The plasticity MD simulations in multi-principal element alloys.</span></p> <p><span>2) MoTaVW-radiation: The primary radiation damage MD simulations in multi-principal element alloys.</span></p> <p><span>3) Goldene: Comparisons between UNEP-v1 and EAM models in MD simulations.</span></p> <p><span>4) NiAlMo: Comparisons between UNEP-v1 and EAM models in MCMD simulations.<br>5) AlCrCuNiV: Comparisons between UNEP-v1 and EAM models in MCMD simulations.</span></p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.