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30 results for “interatomic potential”
Data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler.
<p>This file system package contains data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler. It presents three quantum chemically calculated data sets ("MP2", "DMol", and "ZBL pair-specific") for diatomic interatomic potentials in the repulsive region, where the separation of the atoms is so short that the potential energy is >> 10 eV. The set also contains the fitted parameters for analytical NLH repulsive potentials that consist of a Coulomb term multiplied by a three-exponential screening function.</p> <p>The version from Oct 9, 2025 has updated NLH parameters for the pair Na-O (Z1=8, Z2=11). Otherwise the data is identical to before.</p> <p>The MP2 data sets contain potential data for all elements pairs Z1+Z2<=36, and the DMol, ZBL pair-specific and NLH potentials are proved for all elements pairs Z1, Z2 <=92.</p> <p>The data sets are arranged in the following directories:</p> <p>mp2/ : Data sets for the Hartree-Fock Moller-Plesset2 level calculations</p> <p>dmol/ : Data sets for the Density Functional Theory calculations with the DMOL code</p> <p>nlh/ : Coefficients for the fits of the Nordlund-Lehtola-Hobler potential to the DMol data sets</p> <p>zbl/ : Data sets for the pair-specific Ziegler-Biersack-Littmark potential calculations</p> <p>zbluniv/ : A directory with a Linux bash/awk script that generates the ZBL universal potential.</p> <p>zblspec/ : Coefficients for the pair-specific ZBL screening functions as contained in SRIM-2013.</p> <p>Each subdirectory has its own README.txt file giving additional details on the content of the directory and its subdirectories.</p>
GAP general purpose interatomic potential for iron
<p>A general purpose Gaussian Approximation Potential (GAP) [1,2] for iron. The training database has been computed at the PBE level of theory [3] using the VASP code [4-7]. Fitting of the potentials was done using QUIP/GAP [1,2,8].</p> <p>The potential uses 2-body (distance_2b), 3-body (angle_3b) and SOAP (soap_turbo) [9,10] descriptors, as implemented in the TurboGAP code [11].</p> <p>All files necessary to use the potential QUIP/GAP (compiled with the TurboGAP libraries) are in <em>QUIP_files.tar.gz</em>, all files to use with TurboGAP in <em>TurboGAP_files.tar.gz</em>. The database used to train the potential is in <em>training_database.tar.gz</em>.</p> <p>More details can be found in this publication:</p> <blockquote> <p>Searching for iron nanoparticles with a general-purpose Gaussian approximation potential</p> <p>Richard Jana, Miguel A. Caro</p> <p>https://doi.org/10.1103/PhysRevB.107.245421</p> </blockquote> <p>The authors are grateful to the Academy of Finland for financial support under projects #321713 (R. J. & M.A. C.) and #330488 (M.A. C.), and CSC -- IT Center for Science as well as Aalto University's Science-IT Project for computational resources.</p>
GAP interatomic potential for gold
<p><strong>Gaussian approximation potential</strong> (GAP) for <strong>gold</strong> [1]. It has been fitted with <strong>QUIP/GAP </strong>[1,2] by generating a new database of atomic structures containing:</p> <ol> <li>dimers;</li> <li>fcc, bcc, hcp and simple-cubic supercells, including strained, distorted and high-temperature configurations;</li> <li>surface slabs;</li> <li>clusters.</li> </ol> <p>The calculations were carried out at the <strong>PBE</strong> level of theory [3] using the VASP code [4,5]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>SOAP-type descriptors</strong> (soap_turbo) [6,7], as implemented in the <strong>TurboGAP</strong> code [8]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and TurboGAP. When using this potential, please read and cite:</p> <blockquote> <p><strong>J. Kloppenburg, A. Pedersen, K. Laasonen, M. A. Caro, and H. Jónsson</strong></p> <p>"Reassignment of magic numbers for icosahedral Au clusters: 310, 564, 928 and 1426"</p> <p><a href="https://doi.org/10.1039/D2NR01763F">Nanoscale 14, 9053 (2022)</a></p> </blockquote> <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>LibAtoms: <a href="https://libatoms.github.io/">https://libatoms.github.io</a></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> </ol>
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>
Atomic Cluster Expansion for a General-Purpose Interatomic Potential of Magnesium
<p>This collection contains files associated with Physical Review Materials. "Atomic cluster expansion for a general-purpose interatomic potential of magnesium" (2023) paper:</p><p>- ACE potentials for magnesium.</p><p>-Active set inverted (ASI) for the ACE potential</p><p>- Magnesium DFT-PBE dataset computed with FHI-aims and that was used for fitting Atomic Cluster Expansion potential for magnesium.</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>
GAP interatomic potential for silicon
<p><strong>Gaussian approximation potential</strong> (GAP) for <strong>silicon</strong> [1]. It has been fitted with <strong>QUIP/GAP </strong>[1,2] by recomputing the <strong>Si database of Bartók et al.</strong> [3] at the <strong>PW91</strong> level of theory [4] using the VASP code [5,6,7]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>3-body</strong> (angle_3b) descriptors [5] plus <strong>SOAP-type descriptors</strong> (soap_turbo) [9,10], as implemented in the <strong>TurboGAP</strong> code [11]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and TurboGAP. More details will follow in a scientific<br> publication in due course (bibligraphical data will be added as it becomes available).</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>LibAtoms: <a href="https://libatoms.github.io">https://libatoms.github.io</a></li> <li>A.P. Bartók, J. Kermode, N. Bernstein, and G. Csányi. Phys. Rev. X 8, 041048 (2018).</li> <li>J.P. Perdew and Y. Wang. Phys. Rev. B 45, 13244 (1992).</li> <li>V.L. Deringer and G. Csányi. Phys. Rev. B 95, 094203 (2017).</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>T. Bucko, S. Lebègue, T. Gould, and J.G. Ángyán, J. Phys.: Condens. Matter 28, 045201 (2016).</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> </ol>
Sintering of alumina nanoparticles: comparison of interatomic potentials, molecular dynamics simulations, and data analysis
<p>This is the dataset for the publication in MSMSE 2022 containing all plot scripts and data for reproducing all figures. The dataset is a snapshot of the repository https://gitlab.com/computational-materials-science/public/publication-data-and-code/2022_MSMSE_Roy_et_al_MD-sintering (SHA 7ad2f421deb055f3384c00ba29f2fb1acd0e78ea) that might contain additional/newer data and scripts.</p>
Research data for "Indirect learning and physically guided validation of interatomic potential models"
<p>This dataset contains structural data, potential parameter files, and data shown in the plots for the publication "Indirect learning and physically guided validation of interatomic potential models". Details of the contents can be found in README.txt.</p>
GAP interatomic potential for SnOx nanoparticles
<p><strong>Gaussian approximation potential</strong> (GAP) [1] for <strong>SnOx </strong>nanoparticles. It has been fitted with <strong>QUIP/GAP </strong>[1,2] by generating a new database of atomic structures containing:</p> <ol> <li>dimers Sn-Sn, Sn-O, O-O;</li> <li>Sn, SnO, SnO2 bulk structures;</li> <li>surface slabs;</li> <li>nanoparticles (SnO_(93.8-81.3)%, SnO_50%, SnO_(19-3.1)%, Sn)</li> </ol> <p>The calculations were carried out at the <strong>PBE</strong> level of theory [3] using the VASP code [4,5]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>SOAP-type descriptors</strong> (soap_turbo) [6,7]. The files can be used both with QUIP/GAP (compiled with the soap_turbo libraries) and <strong>TurboGAP</strong> [8]. This is the reference publication for this potential:</p> <p><strong>Junjie Shi, Paulina Pršlja*, Benjin Jin, Milla Suominen, Jani Sainio, Hua Jiang, Nana Han, Daria Robertson, Janez Košir, Miguel Caro, and Tanja</strong><br><strong>Kallio*</strong>. "Experimental and Computational Study Towards Identifying Active Sites of Supported SnOx Nanoparticles for Electrochemical CO2 Reduction Using Machine-Learned Interatomic Potentials". <em>Small</em> 2024, 2402190</p> <p> </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>LibAtoms: <a href="https://libatoms.github.io/">https://libatoms.github.io</a></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> </ol>
GAP interatomic potential for C- and H-based systems
<p> </p> <p>This is a general-purpose Gaussian approximation potential (GAP [1]) for carbon and hydrogen based materials (CH). The potential is capabe of simulating various materials and molecules composed of C and H elements. The interatomic potential has been fitted with <strong>QUIP/GAP</strong> [1,2] using an extensive dataset of different configurations at the PBE level of theory [3] using the VASP code [4,5]. The dataset contains following structures :</p> <ul> <li>Dimers of carbon and hydrogen</li> <li>Trimers</li> <li>CH containing "soup" structures generated during iterative training</li> <li>QM9[6] molecules augmented to C and H containing molecules only</li> <li>Interactive molecules generated using active learning</li> <li>a-C dataset from [7]</li> <li>Bulk and surface carbon structures</li> <li>CH structures geneated using high pressure </li> </ul> <p>This potential includes van der Waals (vdW)<strong> </strong>corrections at the Tkatchenko-Scheffler (TS) level of theory [8] via a machine learning based local parametrization of dispersion interactions [9]. </p>
Dataset for CH GAP interatomic potential
<p>This is a dataset used to train general-purpose CH GAP interatomic potential [1].</p> <p>The database contains the following entries:</p> <ul> <li>Dimers of carbon and hydrogen</li> <li>Trimers</li> <li>"Soup" structures generated during iterative training</li> <li>QM9[6] molecules augmented to C and H containing molecules only</li> <li>Interactive molecules generated using active learning</li> <li>a-C dataset from [2]</li> <li>Bulk and surface carbon structures</li> <li>CH structures geneated using high pressure </li> </ul>
Subset of stochastically generated interacting molecules for CH GAP interatomic potential
<p>This is a subset of the dataset used to train general-purpose CH GAP interatomic potential [1].</p> <p>This subset contains the interacting molecules generated stochastically in a following manner. The subset is generated using active learning and uncertainty-based configuration selection. We started with randomly chosen pairs of CH-containing molecules from the QM9 database up to 7 carbon atoms. The probability of selecting the molecules is set based on the energy and size of the molecule. The probability is lower as the energy above the convex hull is higher. A bigger size of the molecule also lowers the probability to favor the inclusion of small structures. Then, we estimate the uncertainties for the new structures based on how far away from the existing interacting molecules in the training set they are (in configuration space), and identify those with the largest expected errors. This way, we generated about 3k structures. </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>
GAP interatomic potential for amorphous carbon
<p><strong>Gaussian approximation potential</strong> (GAP) for <strong>amorphous carbon</strong> [1]. It has been fitted with <strong>QUIP/GAP</strong> [1,2] by recomputing the <strong>a-C database of Deringer and Csányi</strong> [3] at the <strong>PBE+MBD</strong> level of theory [4,5] using the VASP code [6,7,8]. This potential uses <strong>2-body</strong> (distance_2b) and <strong>3-body</strong> (angle_3b) descriptors [3] plus <strong>SOAP-type descriptors</strong> (soap_turbo) [9,10], as implemented in the <strong>TurboGAP</strong> code [11]. The files can be used both with QUIP/GAP (compiled with the TurboGAP libraries) and TurboGAP. More details will follow in a scientific publication in due course (bibligraphical data will be added as it becomes available).</p> <p>Changes introduced in version 2 of this potential:</p> <ul> <li>More dimer configurations</li> <li>More graphite configurations</li> <li>A tabulated "core potential" to account for short-range repulsion and long-range dispersion interactions explicitly</li> </ul> <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>LibAtoms: <a href="http://libatoms.github.io">https://libatoms.github.io</a></li> <li>V.L. Deringer and G. Csányi. Phys. Rev. B 95, 094203 (2017).</li> <li>J.P. Perdew, K. Burke, and M. Ernzerhof. Phys Rev. Lett. 77, 3865 (1996).</li> <li>A. Tkatchenko, R.A. Di Stasio, R. Car, and M. Scheffler, Phys. Rev. Lett. 108, 236402 (2012).</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>T. Bucko, S. Lebègue, T. Gould, and J.G. Ángyán, J. Phys.: Condens. Matter 28, 045201 (2016).</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> </ol>
Supporting data for "Machine-Learnt Interatomic Potentials for Amorphous Zeolitic Imidazolate Frameworks"
<p>Trajectories from several ab initio molecular dynamics simulations of ZIF-4, produced by CP2K. They consist of NVT simulations of 60 to 80 ps, performed at various temperatures and volumes.</p><p>Volume deformations (from the reference crystal volume) are denoted as volume difference in % compared to the initial volume (e.g. volume_deformation2 has a volume (1 + 0.02) times larger than the initial volume).</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>
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