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135 results for “DFT”
Elasticity tensors of 10276 crystals from DFT computations
<h2>Paper introducing this dataset</h2> <p>Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. <em>Digital Discovery</em>. DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3DD00233K">https://doi.org/10.1039/D3DD00233K</a></p> <p> </p> <p>This dataset consists of three data files in the json format. Each file is explained below.</p> <h2>crystal_elasticity_tensor.json</h2> <p>DFT computed elastic tensors of 10276 crystals used for developing the MatTen model.</p> <p>structure: crystal structure of the material<br>formula_pretty: chemical formula<br>crystal_system: crystal system<br>elastic_tensor: full fourth-rank elastic tensor<br>elastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensor<br>split: split of the data into train, validation, and test subsets for model development</p> <h2><br>max_directional_E.json</h2> <p>New crystals with large maximum directional Young's modulus.</p> <p>material_id: Materials Project identifier<br>formula_pretty: chemical formula</p> <p>structure_original: crystal structure from the Materials Project database<br>elastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original`<br>max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original`</p> <p>structure: further DFT optimized structure with a tigher criterion<br>elastic_tensor: DFT elastic tensor corresponding to `structure`<br>max_directional_E: DFT maximum directional Young's modulus using `structure`<br>elastic_tensor_matten: MatTen predicted elastic tensor using `structure`<br>max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure`</p> <h2><br>elemental_cubic_metal_max_E_along_100_direction.json</h2> <p>New crystals with its maximum directional Young's modulus along the [100] direction.</p> <p>material_id: Materials Project identifier<br>formula_pretty: chemical formula</p> <p>structure_original: crystal structure from the Materials Project database<br>elastic_tensor_matten: MatTen predicted elastic tensor using `structure_original`<br>Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten`</p> <p>structure: further DFT optimized structure with a tigher criterion<br>elastic_tensor: DFT elastic tensor corresponding to `structure`<br>Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`</p>
Enhancing the ReaxFF DFT database
<h1>Enhancing the ReaxFF DFT database</h1> <p>This repository contains the database used to re-parametrize the ReaxFF force field for LiF, an inorganic compound. The purpose of the database is to improve the accuracy and reliability of ReaxFF calculations for LiF. The results and method used were published in the article <a href="https://doi.org/10.1038/s41598-023-50978-5">Enhancing ReaxFF for Molecular Dynamics Simulations of Lithium-Ion Batteries: An interactive reparameterization protocol</a>.</p> <p>This database was made using the simulation obtained using the protocol published in <a href="https://github.com/paolodeangelis/Enhancing_ReaxFF">Enhancing ReaxFF repository</a>.</p> <h2>Installation</h2> <p>To use the database and interact with it, ensure that you have the following Python requirements installed:</p> <p><strong>Minimum Requirements:</strong></p> <ul> <li>Python 3.9 or above</li> <li>Atomic Simulation Environment (ASE) library</li> <li>Jupyter Lab</li> </ul> <p><strong>Requirements for Re-running or Performing New Simulations:</strong></p> <ul> <li>SCM (Software for Chemistry & Materials) Amsterdam Modeling Suite</li> <li>PLAMS (Python Library for Automating Molecular Simulation) library</li> </ul> <p>You can install the required Python packages using pip:</p> <pre><code>pip install -r requirements.txt</code></pre> <blockquote> <p><strong>Warning</strong></p> <p>Make sure to have the appropriate licenses and installations of SCM Amsterdam Modeling Suite and any other necessary software for running simulations.</p> </blockquote> <h2>Folder Structure</h2> <p>The repository has the following folder structure:</p> <pre><code>. ├── CONTRIBUTING.md ├── CREDITS.md ├── LICENSE ├── README.md ├── requirements.txt ├── assets ├── data │ ├── LiF.db │ ├── LiF.json │ └── LiF.yaml ├── notebooks │ ├── browsing_db.ipynb │ └── running_simulation.ipynb └── tools ├── db ├── plams_experimental └── scripts</code></pre> <ul> <li><code>CONTRIBUTING.md</code>: This file provides guidelines and instructions for contributing to the repository. It outlines the contribution process, coding conventions, and other relevant information for potential contributors.</li> <li><code>CREDITS.md</code>: This file acknowledges and credits the individuals or organizations that have contributed to the repository.</li> <li><code>LICENSE</code>: This file contains the license information for the repository (CC BY 4.0). It specifies the terms and conditions under which the repository's contents are distributed and used.</li> <li><code>README.md</code>: This file.</li> <li><code>requirements.txt</code>: This file lists the required Python packages and their versions. (see <a href="#installation">installation section</a>)</li> <li><code>assets</code>: This folder contains any additional assets, such as images or documentation, related to the repository.</li> <li><code>data</code>: This folder contains the data files used in the repository. <ul> <li><code>LiF.db</code>: This file is the SQLite database file that includes the DFT data used for the ReaxFF force field. Specifically, it contains data related to the inorganic compound LiF.</li> <li><code>LiF.json</code>: This file provides the database metadata in a human-readable format using JSON.</li> <li><code>LiF.yaml</code>: This file also contains the database metadata in a more human-readable format, still using YAML.</li> </ul> </li> <li><code>notebooks</code>: This folder contains Jupyter notebooks that provide demonstrations and examples of how to use and analyze the database. <ul> <li><code>browsing_db.ipynb</code>: This notebook demonstrates how to handle, select, read, and understand the data points in the <code>LiF.db</code> database using the ASE database Python interface. It serves as a guide for exploring and navigating the database effectively.</li> <li><code>running_simulation.ipynb</code>: In this notebook, you will find an example of how to get a data point from the <code>LiF.db</code> database and use it to perform a new simulation. The notebook showcases how to utilize either the <a href="https://www.scm.com/doc/plams/index.html">PLAMS</a> library or the <a href="https://www.scm.com/doc/plams/interfaces/amscalculator.html">AMSCalculator</a> and ASE Python library to conduct simulations based on the retrieved data and then store it as a new data point in the <code>LiF.db</code> database. It provides step-by-step instructions and code snippets for a seamless simulation workflow.</li> </ul> </li> <li><code>tools</code>: This directory contains a collection of Python modules and scripts that are useful for reading, analyzing, and re-running simulations stored in the database. These tools are indispensable for ensuring that this repository adheres to the principles of <strong>I</strong>nteroperability and <strong>R</strong>eusability, as outlined by the <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a>. <ul> <li><code>db</code>: This Python module provides functionalities for handling, reading, and storing data in the database.</li> <li><code>plasm_experimental</code>: This Python module includes the necessary components for using the <code>AMSCalculator</code> with PLASM and the SCM software package, utilizing the ASE API. It facilitates running simulations, and performing calculations.</li> <li><code>scripts</code>: This directory contains additional scripts for advanced usage scenarios of this repository.</li> </ul> </li> </ul> <h2>Interacting with the Database</h2> <p>There are three ways to interact with the database: using the ASE db command line, the web interface, and the ASE Python interface.</p> <h3>ASE db Command-line</h3> <p>To interact with the database using the ASE db terminal command, follow these steps:</p> <ol> <li> <p>Open a terminal and navigate to the directory containing the <code>LiF.db</code> file.</p> </li> <li> <p>Run the following command to start the ASE db terminal:</p> <pre><code>ase db LiF.db</code></pre> </li> <li> <p>You can now use the available commands in the terminal to query and manipulate the database. More information can be found in the <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE database documentation</a>.</p> </li> </ol> <h3>Web Interface</h3> <p>To interact with the database using the web interface, follow these steps:</p> <ol> <li> <p>Open a terminal and navigate to the directory containing the <code>LiF.db</code> file.</p> </li> <li> <p>Run the following command to start the ASE db terminal:</p> <pre><code>ase db -w LiF.db</code></pre> </li> <li> <p>Open your web browser and connect to the local server at <a href="http://127.0.0.1:5000">http://127.0.0.1:5000</a>.</p> </li> </ol> <blockquote> <p><strong>Warning</strong></p> <p>To visualize the 3D structure of the system, you need to install the <a href="https://jmol.sourceforge.net/">JMOL extension</a>. You can use the script <code>tools/scripts/install_jmol.py</code> to automatically download and install it:</p> <pre><code>cd tools/scripts/ python install_jmol.py</code></pre> </blockquote> <h3>ASE Python Interface</h3> <p>To interact with the database using the ASE Python interface, you can use the following example code:</p> <pre><code>from ase.db import connect # Connect to the database db = connect("LiF.db") # Query the database results = db.select("success=True") # Iterate over the results for row in results: print(f"ID: {row.id}, Energy: {row.energy}")</code></pre> <div> <pre>For a more detailed example, refer to the notebook <code>notebooks/browsing_db.ipynb</code>. To learn how to perform a simulation, check the notebook <code>notebooks/running_simulation.ipynb</code>.</pre> </div> <h2>Contributing</h2> <p>If you would like to contribute to the Enhancing ReaxFF DFT Database by performing new simulations and expanding the database, please follow the guidelines outlined in the <a href="CONTRIBUTING.md">Contribution Guidelines</a>. You are welcome to submit pull requests or open issues in the repository. Your contributions are greatly appreciated!</p> <h2>How to Cite</h2> <p>If you use the database or the tools provided in this repository for your work, please cite it using the following BibTeX entries:</p> <pre><code>@article{deangelis2023enhancing, title={Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol}, author={De Angelis, Paolo and Cappabianca, Roberta and Fasano, Matteo and Asinari, Pietro and Chiavazzo, Eliodoro}, journal={Scientific Reports}, volume={14}, number={1}, pages={978}, year={2024}, publisher={Nature Publishing Group UK London} }</code></pre> <pre><code>@dataset{EnhReaxFFdatabase, author = {De Angelis, Paolo and Cappabianca, Roberta and Fasano, Matteo and Asinari, Pietro and Chiavazzo, Eliodoro}, title = {{Enhancing the ReaxFF DFT database}}, month = may, year = 2023, publisher = {Zenodo}, version = {1.0.0-beta}, doi = {10.5072/zenodo.1204707}, url = {https://doi.org/10.5281/zenodo.7959121} }</code></pre> <div> <h2>License</h2> </div> <p>The contents of this repository are licensed under the <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <h2>Acknowledgments</h2> <p>This project has received funding from the European Union's <a href="https://ec.europa.eu/programmes/horizon2020/en">Horizon 2020 research and innovation programme</a> under grant agreement <a href="https://cordis.europa.eu/project/id/957189">No 957189</a>. The project is part of <a href="https://battery2030.eu/">BATTERY 2030+</a>, the large-scale European research initiative for inventing the sustainable batteries of the future.</p> <p>The authors also acknowledge that the simulation results of this database have been achieved using the <a href="https://prace-ri.eu/hpc-access/deci-access/">DECI</a> resource <a href="https://www.archer2.ac.uk/">ARCHER2</a> based in UK at <a href="https://www.epcc.ed.ac.uk/">EPCC</a> with support from the <a href="https://prace-ri.eu/">PRACE</a> aisbl.</p>
Coverage-Dependent Stability of RuxSiy on Ru(0001): A Comparative DFT and XPS Study
<p>This repository contains the library of computational structures generated and used for our study "<span>Coverage-dependent stability of Ru<sub><span>x</span></sub>Si<sub><span>y</span></sub> on Ru(0001): a comparative DFT and XPS study</span>" (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CP04069D">https://doi.org/10.1039/D4CP04069D</a>). The final processed data is compiled into a single ASE-compatible database file (https://wiki.fysik.dtu.dk/ase/ase/db/db.html), `RuSi-PCCP-Data.db`.<br><br></p> <p> </p>
Supplementary Material Containing DFT Structure Files and Convergence Tests for our μSR study on Fe2O3
<p>Supplemental material for <em>Local Electronic Structure and Dynamics of Muon-Polaron Complexes in Fe<sub>2</sub>O<sub>3</sub></em></p> <p>M. H. Dehn,<sup>1,2,3</sup> J. K. Shenton,<sup>4,*</sup> D. J. Arseneau,<sup>3</sup> W. A. MacFarlane,<sup>2,3,5</sup> G. D. Morris,<sup>3</sup> A. Maigné,<sup>2</sup> N. A. Spaldin<sup>4</sup> and R. F. Kiefl<sup>1,2,3</sup></p> <p><sup>1</sup>Department of Physics and Astronomy, University of British Columbia, Vancouver, BC V6T 1Z1, Canada<br> <sup>2</sup>Stewart Blusson Quantum Matter Institute, University of British Columbia, Vancouver, BC V6T 1Z4, Canada<br> <sup>3</sup>Triumf, Vancouver, BC V6T 2A3, Canada<br> <sup>4</sup>Department of Materials, ETH Zurich, CH-8093 Zürich, Switzerland<br> <sup>5</sup>Department of Chemistry, University of British Columbia, Vancouver, BC, V6T 1Z1, Canada<br> <sup>*</sup> For queries about the supplemental material in this repository contact <a href="mailto:john.shenton@mat.ethz.ch">J. Kane Shenton</a>.</p> <p>In these notebooks we provide supplemental material for our work on understanding the behaviour of muon-polaron complexes in Fe<sub>2</sub>O<sub>3</sub>.</p> <p>We provide VASP input and output files for each of the candidate muon stopping sites and states identified in the paper (also labelled as in the paper). We summarise the muon stopping sites and provide the code for analysing hyperfine tensors in the jupyter notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Muon-site-summary.ipynb"><code>Muon-site-summary.ipynb</code></a>. There one can also find a summary of the <strong>computational details</strong> for the paper.</p> <p>We further provide <code>vasprun.xml</code> files for some of the tests of convergence with respect to plane-wave cutoff energy and k-point sampling density. These tests are summarised in the jupyter notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Convergence_tests-ENCUT-KPOINTS.ipynb"><code>Convergence_tests-ENCUT-KPOINTS.ipynb</code></a>.</p> <p>A major source of uncertainty stems from the choice of Hubbard U<sub>eff</sub> correction. We varied U<sub>eff</sub> in the range 3 − 6 eV to gauge the impact of this parameter on the predicted energies and precession frequencies of the four charge-neutral muon-polaron complex states. This analysis is available in the notebook: <code><a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/muons_wrt_U.ipynb">muons_wrt_U.ipynb</a>.</code> Although the numerical values do vary as a function of U<sub>eff</sub>, the qualitative behaviour as well as the ordering of frequencies and energy differences presented in the paper (corresponding to U<sub>eff</sub> = 4 eV) remain robust throughout the range: 3 − 5 eV which is the range typically employed for Fe <em>d</em> states.</p> <p>Finally, in the notebook: <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3/blob/master/Separating_the_muon-polaron_complex.ipynb"><code>Separating_the_muon-polaron_complex.ipynb</code></a>, we analyse the separation of muon from the polaron in different configurations. Here again we provide the VASP input and output files as well as the code used to analyse these results.</p> <p>These jupyter notebooks may be previewed on <a href="https://github.com/Shenton-supplemental/Muons_in_Fe2O3">github</a> or via the <a href="https://nbviewer.jupyter.org/github/Shenton-supplemental/Muons_in_Fe2O3">jupyter notebook viewer</a>. The latter does a better job of rendering the inline LaTeX and is therefore preferred.</p> <p>Note that all of files are currently compressed to save space. These must be uncompressed before the notebooks will run. In each notebook there is a cell one can run to decompress the files needed for that particular notebook.</p>
DFT-optimized Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014
<p>There are two folders inside the zipped file:</p> <p>- 838 structures (without DDEC partial atomic charges)</p> <p>- 502 structures (with DDEC partial atomic charges)<br> </p> <p> </p>
DFT-computed datasets for cation ordering in double perovskites
<p>This repository has datasets on cation ordering of double perovskites, computed using density functional theory. The README.md file gives descriptions of each of the datasets available via this repository.</p>
DFT Calculated xyz and log Files as well as csv Files for Machine Learning in Support of "Tailoring Phosphine Ligands for Improved C H Activation: Insights from Δ-Machine Learning"
<p>Transition metal complexes have played crucial roles in various homogeneous catalytic processes due to their exceptional versatility. This adaptability stems not only from the central metal ions but also from the vast array of choices of the ligand spheres, which form an enormously large chemical space. For example, Rh complexes, with a well-designed ligand sphere, are known to be efficient in catalyzing the C-H activation process in alkanes. To investigate the structure-property relation of the Rh complex and identify the optimal ligand that minimizes the calculated reaction energy ΔE of an alkane C-H activation, we have applied a Δ-Machine Learning method trained on various features to study 1,743 pairs of reactants (Rh(PLP)(Cl)(CO)) and intermediates (Rh(PLP)(Cl)(CO)(H)(propyl)). Our findings demonstrate that the models exhibit robust predictive performance when trained on features derived from electron density (R<sup>2 </sup>= 0.816), and SOAPs (R<sup>2 </sup>= 0.819), a set of position-based descriptors. Leveraging the model trained on xTB-SOAPs that only depend on the xTB-equilibrium structures, we propose an efficient and accurate screening procedure to explore the extensive chemical space of bisphosphine ligands. By applying this screening procedure, <a>we identify ten newly selected reactant-intermediate pairs with an average ΔE </a>of 33.2 kJ mol<sup>-1</sup>, remarkably lower than the average ΔE of the original data set of 68.0 kJ mol<sup>-1</sup>. This underscores the efficacy of our screening procedure in pinpointing structures with significantly lower energy levels.</p> <p>_______________________________________________________________________</p> <p>The dataset contains three file types:</p> <p>Version 1.0:</p> <ol> <li>xyz files of the final optimized Rh-phosphine complexes; one set for the starting materials denoted as "molecule-XXXX_4-times" and one set for the intermediates after C-H activation denoted as "molecule-XXXX_6-times"</li> <li>Gaussian16 log files for the optimization process; one set for the starting materials denoted as "molecule-XXXX_4-times" and one set for the intermediates after C-H activation denoted as "molecule-XXXX_6-times"</li> <li>csv files containing the per molecule features used for training the different machine learning models. The name of the csv files indicates which property was predicted and which model was used</li> </ol> <p>New in version 1.1 (other data is unchanged):</p> <ol> <li>Gaussian16 log files for the ten newly identified bisphosphine ligands; one set for the product material denoted as "LXX_6-times-axial" and one set for the transition state for the C-H activation denoted as "LXX_C-H-activation_TS"</li> </ol>
Static and Dynamic DFT Data Sets for Polymorphs of l-Cysteine - Stability and Terahertz Spectra
<p>Data sets associated with static and dynamic density functional calculations are reported for the four known polymorphs of l-cysteine and for models associated with the known disorder in Form I.. </p> <p>Static calculations are used to explore the relative free energies (within the harmonic approximation) of the polymorphs as a function of pressure. The energetics for dihedral angle rotation are explored and the barriers for rotation between the hydrogen bonding motifs have been calculated for each polymorph.</p> <p>Molecular dynamics calculations are reported for each polymorph and for models of hydrogen bond disorder which are known to exist at higher temperatures.</p> <p>Finally static and dynamic calculations of the infrared and terahertz spectra are performed.</p>
Th:LiCAF DFT Relaxations
<p>This dataset was generated with VASP and contains OUTCAR files for 1625 relaxations. The paper for this work was published in <a href="https://doi.org/10.1002/adts.202200185">Adv. Theory Simul. (2022)</a>.</p> <p>Relaxations in the folder "not_converged" did not reach the convergence criterion for ionic forces.</p> <p>Not all relaxations were generated with the same INCAR file, but all differences should be irrelevant for the simulation outcome. Specifically, the number of cores which work on an individual orbital (NCORE) and the total number of (empty) bands (NBANDS) are not equal in all relaxations.</p>
DFT Featurization of 730 Commercially Available Boronic Acids
<p>Dataset of DFT calculated features of 730 commercially available boronic acids. </p> <p>Software, scripts, input data, and workflow:</p> <p>https://github.com/Gademann-UZH/Chemical-Space-Generation</p> <p>Archived version, see</p> <p>DOI: 10.5281/zenodo.7540235</p> <p>Publication, see:</p> <p>Mechanistic Studies and Data Science-Guided Exploration of Bromotetrazine Cross-Coupling<br> Lukas V. Hoff, Gleb A. Chesnokov, Anthony Linden, and Karl Gademann<br> ACS Catalysis 2022 12 (15), 9226-9237<br> DOI: 10.1021/acscatal.2c01813 </p> <p> </p> <p>V1.1: Corrected calculations for OB(O)C1=CC=C(C=C1)C(=C(/C1=CC=CC=C1)C1=CC=C(C=C1)B(O)O)\C1=CC=CC=C1 and for OB(O)C1=CC(F)=C(OCCCN2CCOCC2)C=C1.</p>
DFT optimised structure used for the paper "Cation Insertion to Break the Activity/Stability Relationship for Highly Active Oxygen Evolution Reaction Catalyst"
<p>DFT optimised structures used to calculate the OER activities in "Cation Insertion to Break the Activity/Stability Relationship for Highly Active Oxygen Evolution Reaction Catalyst". The structures are bundled in two databases, LiIrO3.db which contains all structures for alpha-LiIrO<sub>3</sub> and KLiIrO3-disordered.db which contains all the structures for the disordered Li<sub>0.75</sub>K<sub>0.25</sub>(H<sub>2</sub>O)<sub>0.50</sub>IrO<sub>3 </sub>structure. The structures can be retrieved using the Atomic Simulation Environment (ASE, https://wiki.fysik.dtu.dk/ase/index.html). The keywords 'ads' and 'surface' can be used to search the structure, e.g. surface='Z-step' and ads='*OOH' will give the structure with OOH adsorbed on the Z-step surface (see paper for details on the different surfaces).</p>
Data accompanying publication: "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction"
<p>Embeddings and raw files to complement the paper "General Chemically Intuitive Atom-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction". The embeddings should be all the data needed for full reproducibility of the results published. The GitHub repo GeneralDFT (https://github.com/moleculebits/GeneralDFT) contains the python scripts required to make use of the data, along with some basic plotting functionalities.</p>
Dataset for DFT and one-step model results used in publication "Persistence of Structural Distortion and Bulk Band Rashba Splitting in SnTe above Its Ferroelectric Critical Temperature" in Nano Letters, 2024, 24, 1, 82–88
<p>Dataset for DFT and one-step model results used in publication DOI 10.1021/acs.nanolett.3c03280, "Persistence of Structural Distortion and Bulk Band Rashba Splitting in SnTe above Its Ferroelectric Critical Temperature" in Nano Letters, 2024, 24, 1, 82–88.</p> <p>Description of dataset is in the ReadMe.txt file in subdirectories.</p>
EXCEED-DMv0.2.8: DFT-computed electronic wave functions for Si and Ge
<p>Wave function coefficients, with and without the all-electron reconstruction, for Si and Ge on a 10x10x10 uniform k mesh. For use with EXCEED-DM to compute Dark Matter induced electronic excitation rates.</p> <p>Note:</p> <p>Compatible with EXCEED-DMv0.2.8</p>
Data for Microkinetic modeling of the transient CO2 methanation with DFT-based uncertainties in a Berty reactor
<p>Dataset and scripts for the manuscript "Microkinetic modeling of the transient CO2 methanation with DFT-based uncertainties in a Berty reactor", which has been submitted for review. The file contains all the raw data and the evaluation of the experiments. Additionally, all scripts for the microkinetic model are provided to perform transient simulations with all 5000 methanation mechanisms investigated in the manuscript.</p>
Iron Nitride Nanoparticles for Rapid Dechlorination of Mixed Chlorinated Ethene Contamination: DFT Calculations
<p>This dataset contains input and output files of density functional theory calculations on which the computational part of the peer-reviewed article "Iron nitride nanoparticles for rapid dechlorination of mixed chlorinated ethene contamination" by M. Brumovský et al., doi 10.1016/j.jhazmat.2022.129988, is based. Please cite this article when using the dataset.</p> <p>The dataset includes:<br> * Structures and energies of adsorbed cis-DCE and PCE molecules on the Fe(110), Fe4N(001), and FeS(001) surfaces in the folder "Adsorption_calculations", including structures and energies of adsorbed TCE on the FeS(001) surface<br> * cis-DCE and PCE homolytic bond dissociation energies calculated using VASP and TURBOMOLE in the folder "BDE_calculations"<br> * Relaxed structures of dechlorinated intermediates on the Fe(110), Fe4N(001), and FeS(001) surfaces in the folder "Intermediates"<br> * Transition state calculations of chloroethene dechlorination reactions, including frequency calculations of transitions states, in the folder "NEB_calculations"<br> * Energies of reactants, products, and transitions states calculated with the inclusion of the solvent effect in the folder "VASPsol_calculations"</p> <p>The final geometries and energies calculated using VASP are reported in the CONTCAR and OUTCAR files, respectively. Note that the POTCAR files are not allowed to be made publicly accessible. However, their description is given in the OUTCAR files. Please consult VASP/TURBOMOLE manual for more information regarding input and output files.</p> <p>The fully relaxed unit cells of Fe, Fe4N, and FeS, as well as adsorption calculations of TCE, and transition state calculations of TCE chemisorption and its first dechlorination step on the Fe4N(001) surface are accessible in a previously published dataset, doi 10.5281/zenodo.6338412.</p> <p>Funding: This work was supported by the Austrian Science Fund (FWF) project M 2892-N. The Vienna Scientific Cluster (Project No. 70544) is gratefully acknowledged for providing computational resources.</p> <p>Terms of use: These data are provided "as is", without any warranty. The data are provided under the Creative Commons Attribution 4.0 International license.</p>
Data & Codes used in: Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)
<p>Here we provide the data, codes, and outline the procedure to reproduce the results presented in the paper "Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)" by E. Ventura-Macias, P. M. Martinez, Rubén Pérez, and J. G. Vilhena.</p> <p>The following sections contain a detailed description of the data and codes. At the end of this README, you will find instructions on how to generate the classical force-field parameters (Morse and Lennard-Jones) from the potential energy surfaces (PES) computed at the DFT level.</p> <p>The procedure is general and applies to any given pair of molecule and surface. The provided codes will allow you to swiftly generate the PES at the DFT level, fit the Lennard-Jones and Morse potentials, and test them in a LAMMPS MD simulation.</p> <p>For a thorough explanation of the procedure and the relevance of these results, please refer to the original publication (ARTICLE_DOI).</p> <p>If you find this helpful, please consider citing the article (ARTICLE_DOI).</p> <h2>Data structure</h2> <p>The data is organized in the following way:</p> <ol> <li> <p>DFT</p> <ul> <li>The equilibrium adsorption geometry of methanethiol (MTH), propanethiol (PTH), and cysteine (CYS) for the Au-mol configuration with PBE+DFT-D3.</li> <li>Potential Energy Surface (PES) computed at the DFT level (Figure 3 of the main manuscript).</li> <li>The scripts used to generate the PES.</li> </ul> </li> <li> <p>MD</p> <ul> <li>Fitting code and general instructions on how to use it.</li> <li>General input scripts used to generate MD data within LAMMPS.</li> </ul> </li> </ol> <h3>DFT Data</h3> <p>The DFT data in <code>DFT.zip</code> is organized in the following way:</p> <ul> <li> <p><code>DFT_PES/</code></p> <p>This folder contains the DFT potential energy surfaces (PES) for the interaction of the sulfur atom of methanethiolate (<code>mth</code>), propanethiolate (<code>pth</code>), and cysteine (<code>cys</code>) with the Au(111) surface and the necessary scripts to reproduce it.</p> <ul> <li> <p><code>results/</code></p> <p>The PES are given in one csv file per molecule named as <em><code>mol</code></em> + <code>_PES_S-Au111.csv</code>, where <em><code>mol</code></em> is the molecule name. Columns are as follows:</p> <table> <tbody> <tr> <td><strong>Label</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><em>i</em></td> <td>calculation number</td> </tr> <tr> <td><em>site</em></td> <td>adsorption site</td> </tr> <tr> <td><em>z</em></td> <td>distance of the S atom to the surface</td> </tr> <tr> <td><em>deltaz</em></td> <td>distance difference from the minimum energy position of the S atom</td> </tr> <tr> <td><em>pbed3</em></td> <td>PBE+D3 binding energy</td> </tr> <tr> <td><em>pbe</em></td> <td>PBE component of the binding energy</td> </tr> <tr> <td><em>d3</em></td> <td>DFT-D3 component of the binding energy</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li> <ul> <li> <p><code>mth/</code> | <code>pth/</code> | <code>cys/</code></p> <p>Each folder contains the CONTCAR (VASP) file for the optimized geometry of the molecule adsorbed on the Au(111) surface with PBE+D3.</p> </li> <li> <p><code>setup_grid.py</code></p> <p>Python script to set up the POSCAR files for the PES calculations.</p> </li> <li> <p><code>read_results.py</code></p> <p>Python script to read the results of the PES calculations.</p> </li> <li> <p><code>sub_array.sh</code></p> <p>Bash script to submit the PES calculations to an SLURM-based cluster.</p> </li> <li> <p><code>INCAR</code> | <code>KPOINTS</code> | <code>surf.CONTCAR</code></p> <p>VASP input files for the PES calculations.</p> </li> </ul> </li> </ul> <h3>MD fitting</h3> <p>The MD data in MD.zip is are organized as:</p> <ul> <li> <p><code>Fitting/</code></p> <ul> <li><code>optimize_Morse.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of Morse potential.</p> <ul> <li><code>optimize_LJ.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of LJ potential.</p> </li> <li> <p><code>Histogram/</code></p> <ul> <li><code>in.test</code></li> </ul> <p>LAMMPS input script to extract an XY file for the position of the S atom in an NVT simulation.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>sheng.eam</code></li> </ul> <p>EAM potential file in case Au dynamics are wished to be included.</p> </li> <li> <p><code>Single_point_scan/</code></p> <ul> <li><code>in.scan</code></li> </ul> <p>LAMMPS input script to perform single-point energy scan of a given molecule.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>launch.sh</code></li> </ul> <p>Launches the single point scan.</p> <ul> <li><code>plot_scan.py</code></li> </ul> </li> <li> <p><code>Molecules</code></p> <p>LAMMPS sample geometries for the 3 molecules.</p> </li> </ul> <h2>Steps to reproduce the results</h2> <p>The PES calculations were performed using the VASP code and two Python scripts for setup and results parsing.</p> <h3>Requirements</h3> <ul> <li>VASP (tested with version 5.4.4) <ul> <li><code>PAW_PBE</code> pseudopotentials set version 5.4</li> </ul> </li> <li>Python3 with packages: <ul> <li>ASE (Atomic Simulation Environment)</li> <li>Numpy</li> <li>Pandas</li> <li>matplotlib (optional)</li> </ul> </li> </ul> <h3>Steps</h3> <p>Each molecule has its own directory with the necessary files to reproduce the results. The following steps are for the methanethiolate molecule (<code>mth</code>).</p> <ol> <li>Set up the grid of points for the PES calculations by running the <code>setup_grid.py</code> script. It will create a subfolder <code>run/</code> inside the molecule's directory with the POSCAR files for each point in the grid.</li> </ol> <blockquote> <p>python setup_grid.py mth</p> </blockquote> <ol> <li> <p>Create the corresponding <code>POTCAR</code> file and place it in the molecule's directory.</p> </li> <li> <p>Change the <code>sub_array.sh</code> script to match the number of calculations in the array numbers and the MOL variable.</p> </li> </ol> <pre><code>#SBATCH --array=1-number of calculations MOL=mth</code></pre> <ol> <li>Submit the calculations to a SLURM-based cluster by running the <code>sub_array.sh</code> script from the <code>DFT_PES</code> directory.</li> </ol> <blockquote> <p>sbatch sub_array.sh</p> </blockquote> <ol> <li>After the calculations are finished, run the <code>read_results.py</code> script to parse the results and generate the PES csv files. It takes the arguments <code>--surf</code> and <code>--mol</code> to specify the surface and molecule PBE+D3 and D3 total energies.</li> </ol> <blockquote> <p>python read_results.py mth --surf -128.5228 -17.7147 --mol -22.6026 -0.0083</p> </blockquote> <h3>Reference values for the PBE+D3 and D3 surface and molecule energies</h3> <table> <tbody> <tr> <td> </td> <td><strong>Surface</strong></td> <td> </td> <td><strong>Molecule</strong></td> <td> </td> </tr> <tr> <td><strong>Molecule</strong></td> <td><strong>PBE+D3<br></strong></td> <td><strong>D3</strong></td> <td><strong>PBE+D3</strong></td> <td><strong>D3</strong></td> </tr> <tr> <td>mth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-22.6026</td> <td>-0.0083</td> </tr> <tr> <td>pth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-55.8615</td> <td>-0.0086</td> </tr> <tr> <td>cys</td> <td>-128.5228</td> <td>-17.7147</td> <td>-74.0338</td> <td>-0.1611</td> </tr> </tbody> </table> <h3>Fitting procedure</h3> <p>Both the .xyz and .csv files should be located at the same folder as the script. Then, simply run the code (<code>mth</code> is used as an example):</p> <blockquote> <p>python optimize_Morse.py mth</p> </blockquote> <p>or</p> <blockquote> <p>python optimize_LJ.py mth</p> </blockquote> <p>The script will print the optimized parameters: [De, re, α] or [ϵ, σ] for Morse or LJ respectively. It will also plot a fitting plot and a birdview of the resulting PES.</p> <h3>Using the potential. Histogram example</h3> <p>The code will run for the optimized <code>mth</code> Morse parameters and extract a <code>occ.lammpstrj</code> containing the (x,y) positions of the S atom throughout the NVT simulation. Note that only Au-S interaction is included. Start as:</p> <blockquote> <p>lmp -in in.test</p> </blockquote> <p>This is easily adaptable to other routines or molecules and is thought to be a generic LAMMPS starting input.</p> <h3>Single-point energy scan</h3> <p>Go to the 2C) folder and launch the scan with:</p> <blockquote> <p>./launch.sh</p> </blockquote> <p>This will create a folder named <code>fine_scan</code> containing 128 folders. Each folder is assigned to an (x,y) position. Then, inside each folder, a single-point energy evaluation is performed at various Z heights around the absolute minima.</p> <p>The <code>in.scan</code> file should be modified accordingly with the appropriate potentials. It is set to perform the scan with the optimized Morse potential by default.</p> <p>The output is gathered in the <code>E_readout.dat</code> folder with the following structure (all energies in eV):</p> <table> <tbody> <tr> <th>Total Energy</th> <th>Intramolecular energy</th> <th>Au-mol vdW interaction energy</th> <th>Au-S interaction energy</th> </tr> </tbody> <tbody> <tr> <td>-133.62</td> <td>0.123112</td> <td>-0.2403</td> <td>-1.31765</td> </tr> <tr> <td>-133.721</td> <td>0.123112</td> <td>-0.28-401</td> <td>-1.37853</td> </tr> </tbody> </table> <p>Therefore, the total adsorption energy will be the sum of the last two columns.</p> <p>Energies are ordered in increasing Z for the same (x,y) point. That is, the first 12 lines correspond to 12 heights of the starting (x,y) coordinate, the next 12 lines to heights at the second (x,y) configuration and so on.</p> <p>The python script <code>plot_scan.py</code> may be used to plot the results. The <code>E_readout.dat</code> file and <code>.csv</code> must be in the same folder.</p> <blockquote> <p>python plot_scan.py mth</p> </blockquote>
Datasets of DFT adsorption energies of H and for O and OH on different pure metals and binary intermetallic compounds considering the application of elastic strains and lists of candidates for screening
<p>This resource contains two datasets and two lists of candidates for screening in JSON format. Also It contains ZIP folders with all Quantum Espresso Inputs and outputs from which the JSON datasets were obtained. All Quantum Espresso outputs will be later added to Catalysis Hub (https://www.catalysis-hub.org/). The file "QuantumEspresso_versions" is a text file contaning the information of the Quantum Espresso versions employed for obtaining the dataset.</p> <p>The datasets contain the adsorption energies for surface slabs of a large number of binary intermetallic compounds with different compositions and lattices (for instance, A3B fcc, A3B hpc, AB bcc, etc.). Adsorption energies were computed for different adsorbates (H, O, and OH) on distinct adsorption sites (e.g., fcc AAB, fcc AAA, hcp AAA, hcp AAB, on-top A, and on-top B) and minimum energy surfaces. In addition, different elastic strains (biaxial tension, biaxial compression) were applied to assess their effect on adsorption energies. All calculations were carried out using DFT approximations as implemented in the Open-source software Quantum Espresso. Besides the adsorption energies, the datasets also contain relevant geometric and electronic descriptors (PSI, cell volume, weighted atomic radius, generalized coordination number, weighted electronegativity, weighted first ionization energy, outer electrons, and biaxial strain) calculated to feed them as features in the training of ML models. The datasets with the tag "scaled" on its name have the descriptors scaled following a MinMax scaling and are given in xlsx format.</p> <p>The lists for screening contain candidates not included in the dataset for which Random Forest predictions of the Eads were obtained. The lists contain the geometric and electronic descriptors of all screening candidates, as well as the predicted adsorption energy (Eads_RF).</p> <p>A GitHub repository is linked to this dataset (https://github.com/vvassilevg/HighHydrogenML). The repository contains two Python scripts:</p> <p>1) Script for creating a dataset from QuantumEspresso outputs, where all relevant descriptors are computed. It outputs a pickle and json files that can be later converted to any other desired format (like xlsx).</p> <p>2) Script for training a Random Forest model for the prediction of adsorption energies (the datasets with the "scaled" tag must be used for the script to work correctly).</p> <p> </p> <p>The dataset, ML model and screening have been accepted for publication in Catalysis Science & Technology DOI: DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D</a>. The accepted Manuscript and the Supplementary information are avilable within this repository.</p> <p> </p> <p>If you use this dataset or any of the files within this repository, please cite the original publication (<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4CY00491D">10.1039/D4CY00491D)</a> in your work.</p>
TD-DFT of Dipyrrolonaphthyridinedione (DPND) Annihilators
<p><span>Molecular geometry of the first excited triplet (T1) and singlet (S1) state energies of DPND derivatives 1-6 were modelled using the quantum chemistry software Gaussian. Density functional theory (DFT) geometry optimization was performed using the B3LYP/6-31G(d) functional/basis set in a vacuum. The T1 and S1 energies were obtained by employing time dependent DFT calculations with the same functional/basis set.</span></p>
BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT
<p>First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes. </p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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