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Dataset results
11 results for “Machine learning interatomic potentials”
Data related to the publication "Efficient molecular dynamics simulations of deep eutectic solvents with first-principles accuracy using machine learning interatomic potentials"
<p>The training data sets, the trained machine learning models, and input scripts for the training and molecular dynamics simulations.</p>
Dataset for "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena"
<p>This dataset was used to fit a general purpose machine-learning interatomic potential for Mo-Si alloys based on the Atomic Cluster Expansion (ACE) formalism. It supports the paper "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena".</p>
Research data for "Exploring the energy landscape of aluminas through machine learning interatomic potential"
<p>This dataset supports the paper "Exploring the energy landscape of aluminas through machine learning interatomic potential". The paper is online here:</p> <p>The following folders are provided:</p> <ul> <li><em>classical_potential_files</em>: Contains all the empirical potentials used in this study.</li> </ul> <p> </p> <ul> <li><strong><em>crystal_structure_file</em></strong>: Contains structural files of aluminas with various crystal structures, which can be distinguished by their respective filenames. Configurations of alumina with partially occupied cation sites can be obtained from the references provided in the supplementary materials of our article.</li> </ul> <p> </p> <ul> <li><strong><em>lowest_E-config</em></strong>: The files named <code>poscar_{0..19}</code> represent the 20 structure files identified through our developed structural search workflow in conjunction with the final NEP of Aluminas. These structures exhibit different distributions of Al cation occupancy sites. The suffix numbers in the file names indicate that these 20 structures are arranged in ascending order based on their corresponding energy values after structural relaxation using the NEP. In other words, <code>poscar_0</code>, after structural optimization, has the lowest energy among these 20 configurations. Additionally, we have included the CIF files for the crystal structures with partial occupancies provided by the Smrcok model in this folder.</li> </ul> <p> </p> <ul> <li><strong><em>the_final-dataset_alumina</em></strong>: This folder contains all the relevant files for training the final NEP of Aluminas, including the final training dataset named <code>train.xyz</code>, the training parameter file <code>nep.in</code>, and log files. The file <code>nep.txt</code> refers to the final NEP of Aluminas. </li> </ul> <p> </p> <ul> <li><strong><em>various_test-datasets</em></strong>: This folder provides all the test datasets used for testing the final NEP of Aluminas. We have categorized them into four types based on composition: clusters, amorphous structures, crystals, and datasets with physically unallowed configurations that exhibit nearest-neighbor cation occupancy according to the Smrcok model.</li> </ul> <p>Additionally, for ease of retrieval, we have placed the file for the final NEP of aluminas in the main directory and named it <code>nep_3335.txt</code>, where the suffix indicates that the final training dataset <code>train.xyz</code> contains 3,335 structures. This file is identical to the file named <code>nep.txt</code> located in the folder <code>the_final-dataset_alumina</code>.</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>
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>
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>
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>
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>
Data related to the publication "structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations"
<p>Reference training and test datasets, trained ML potential models, and input scripts for the training (Allegro) and MD simulations (LAMMPS).</p>
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
<p>This dataset contains a vertical slice of the data used to generate the results found in the publication "Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials"<br> It contains nested sampling input files and trajectory files for each potential studied, as well as the xml files and training data for the new potential, GAP-20U+gr.</p>
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