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30 results for “interatomic potential”
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>
GAP interatomic potential for PtAu:H nanoparticle simulation
<p><strong>Gaussian approximation potential</strong> (GAP) [1] for the <strong>platinum-gold-hydrogen </strong>system. It can be safely used to generate PtAu nanoparticles throughout the whole composition range and to hydrogenate them (add adsorbed H on the surface). For any other system, the accuracy and stability of the potential should be specifically tested (e.g., against reference DFT calculations). It has been fitted with <strong>gap_fit </strong>[2] by generating a new database of atomic structures containing:</p> <ol> <li>bulk PtAu structures, including 3x3x3 supercells with substitutional alloying as determined by cluster-expansion configurations generated by the CASM code [10];</li> <li>PtAu surface slabs, with random alloying and H adsorption at different coverages;</li> <li>iteratively generated PtAu clusters (nanoparticles), following a similar approach as in Ref. [3], and adding H coverage to those.</li> </ol> <p>The calculations were carried out at the <strong>PBE</strong> level of theory [4] using the VASP code [5,6]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>SOAP-type descriptors</strong> (soap_turbo) [7,8]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and <strong>TurboGAP</strong> [9]. The reference publication for this potential will be added in time (e.g., when a preprint is a valable, and then updated when a journal publication is available).</p> <p>Financial support from the Research Council of Finland and computational resources from CSC (the Finnish IT Center for Science) and Aalto University's Science-IT project are gratefully acknowledged.</p> <p><strong>References</strong></p> <ol> <li>A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).</li> <li>S. Klawohn, J.P. Darby, J.R. Kermode, G. Csányi, M.A. Caro, and A.P. Bartók. J. Chem. Phys. 159, 174108 (2023).</li> <li> <div>J. Kloppenburg, L.B. Pártay, H. Jónsson, and M.A. Caro. J. Chem. Phys. 158, 134704 (2023).</div> </li> <li>J.P. Perdew, K. Burke and M. Ernzerhof. Phys. Rev. Lett. 77, 3865 (1996).</li> <li>VASP: <a href="http://vasp.at/">http://vasp.at</a></li> <li>G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).</li> <li>A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).</li> <li>M.A. Caro. Phys. Rev. B 100, 024112 (2019).</li> <li>TurboGAP: <a href="http://turbogap.fi/">http://turbogap.fi</a></li> <li>https://prisms-center.github.io/CASMcode_docs</li> </ol>
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>
Supporting data for "Neural Network-Based Interatomic Potential for the Study of Thermal and Mechanical Properties of Siliceous Zeolites"
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Dismai-Bench Datasets and Interatomic Potentials
<p>Datasets and interatomic potentials for Dismai-Bench, a generative model benchmark for inorganic materials. Dismai-Bench evaluates models on large disordered materials and interfaces, through direct comparisons between training structures and generated structures.</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>
Data from: Evaluation and comparison of classical interatomic potentials through a user-friendly interactive web-interface
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Dataset related to article: Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials
<p>This repository contains the dataset to train and test the GeTe Machine Learning Interatomic Potential (MLIP). The computational details are given in the manuscript. </p>
Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials. Glycine and Acetylacetone Data.
<p>Data generated and analyzed in <strong>M.Kulichenko <em>et. al</em>. Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials. <em>Nat. Comput. Sci </em>(2023).</strong></p> <ul> <li>Glycine data sets (MD-AL and UDD-AL).</li> <li>Acetylacetone MD and UDD trajectories.</li> <li>See README.md for details.</li> </ul> <p> </p> <p> </p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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