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12 results for “Organic batteries”
Calculated state-of-the art results for solvation and ionization energies of thousands of organic molecules relevant to battery design
<p>This dataset presents molecular properties critical for battery electrolyte design, specifically solvation energies, ionization potentials, and electron affinities. The dataset is intended for use in machine learning model testing and algorithm validation. The properties calculated include solvation energies using the COSMO-RS method [1] and ionization potentials and electron affinities using various high-accuracy computational methods as implemented in MOLPRO [2]. Computational details can be found in Ref. [3], with scripts used to generate the data mostly uploaded to our github repository [4].</p> <p>Molecular Datasets Considered:</p> <ul> <li> <p>QM9 Dataset: Contains small organic molecules broadly relevant for quantum chemistry [5]</p> </li> <li> <p>Electrolyte Genome Project (EGP): Focuses on materials relevant to electrolytes.[6]</p> </li> <li> <p>GDB17 and ZINC databases: Offer a broad chemical diversity with potential application in battery technologies. [7, 8]</p> </li> </ul> <h2>Data structure</h2> <p>How to Load the Data:</p> <p>All files can be loaded with</p> <p><br><code>import json</code></p> <p><code>with open("file.json", "r") as f:</code><br><code> data_dict = json.load(f)</code></p> <p><br>and the filestructure can be explored with</p> <p><code>data_dict.keys()</code></p> <p>We have also added an example script in python that shows how to extract all data from the JSON files following this link</p> <p><a href="https://github.com/chemspacelab/VienUppDa/blob/main/SolQuest/BIG_MAP_DATA/load_db.py">How to extract the data</a></p> <p>Note the file structure of the the AMONS JSON files is slightly different as explained below!</p> <h3>Solvation energies</h3> <p>The data is stored in two types of JSON archives: files for full molecules of GDB17 and ZINC and files for amons of GDB17 and ZINC. They are structured differently as amon entries are sorted by the number of heavy atoms in the amon (e.g., all amons with 3 heavy atoms are stored in <code>ni3</code>). Because of the large number of amons with 6 or 7 heavy atoms,they are further split into <code>ni6_1</code>, <code>ni6_2</code>, and so on. A sub dictionary of an amon dictionary or a full molecule dictionary contains the following keys:</p> <p><code>ECFP</code> - ECFP4 representation vector</p> <p><code>SMILES</code> - SMILES string</p> <p><code>SYMBOLS</code> - atomic symbols</p> <p><code>COORDS</code> - atomic positions in Angstrom</p> <p><code>ATOMIZATION</code> - atomization energy in [kcal/mol]</p> <p><code>DIPOLE</code> - dipole moment in Debye</p> <p><code>ENERGY</code> - energy in Hartree</p> <p><code>SOLVATION</code> - solvation energy in [kcal/mol] for different solvents at 300 K.</p> <p> </p> <p>Files:</p> <p> </p> <p><strong><em><code>GDB17.json.zip</code> </em></strong>(unpack with unzip first with unzip <strong><em><code>GDB17.json.zip</code></em></strong>) - subset of GDB17 random molecules</p> <p><strong><em><code>AMONS_ZINC.json</code> </em></strong>-<strong><em> </em></strong>all<strong><em> </em></strong>amons of ZINC up to 7 heavy atoms</p> <p><strong><em><code>EGP.json</code> </em></strong>- EGP molecules</p> <p><code><strong><em>AMONS_GDB17.json</em></strong></code> - all amons of GDB17 up to 7 heavy atoms</p> <p><code><strong>QM9IPEA_raw_molpro_output</strong>.zip</code> - compressed folder with raw Molpro input and output files</p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Description </strong></td> <td><strong>Molecules</strong></td> </tr> <tr> <td>AMONS_GDB17.json</td> <td>GDB17 amons</td> <td>37860</td> </tr> <tr> <td>AMONS_ZINC.json</td> <td>ZINC amons </td> <td>88771</td> </tr> <tr> <td>GDB17.json</td> <td>Subset of GDB17</td> <td>309468</td> </tr> <tr> <td>EGP.json </td> <td>EGP molecules </td> <td>18362</td> </tr> </tbody> </table> <p>Atomic energies $E_{at}$ at BP and def2-TZVPD level in Hartree [Ha]</p> <table> <tbody> <tr> <td><strong>Element</strong></td> <td><strong>H</strong></td> <td><strong>C</strong></td> <td><strong>N</strong></td> <td><strong>O</strong></td> <td><strong>F</strong></td> <td><strong>Br</strong></td> <td><strong>Cl</strong></td> <td><strong>S</strong></td> <td><strong>P</strong></td> </tr> <tr> <td>Eat [Ha]</td> <td>-0.5</td> <td> -37.85</td> <td> -54.60</td> <td> -75.09</td> <td>-99.77</td> <td>-2574.40</td> <td> -460.20</td> <td> -398.16</td> <td>-341.30</td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>B</strong></td> <td><strong>Si</strong></td> </tr> <tr> <td> -24.65</td> <td> -289.40</td> </tr> </tbody> </table> <p>We follow the convention of negative atomization energies for stablity compared to the isolated atoms:</p> <p>$E_{atomization} = E_{mol} - \sum_{i} E_{at,i}$</p> <p><br>Free energy of solvation at 300 K in [kcal/mol]:</p> <h3>Ionization potentials and electron affinities</h3> <p>The upload contains two JSON files, <strong><em>QM9IPEA.json</em></strong> and <strong><em>QM9IPEA_atom_ens.json</em></strong>. <strong><em>QM9IPEA.json </em></strong>summarizes MOLPRO calculation data grouping it along the following dictionary keys:</p> <p> </p> <p><strong>QM9IPEA.json</strong></p> <p><code>COORDS</code> atom coordinates in Angstroms<br><code>SYMBOLS</code> atom element symbols<br><code>ENERGY</code> total energies for each charge (0, -1, 1) and method considered<br><code>CPU_TIME</code> CPU times (in seconds) spent at each step of each part of the calculation<br><code>DISK_USAGE</code> highest total disk usage in GB<br><code>ATOMIZATION_ENERGY</code> atomization energy at charge 0 (all methods)<br><code>IONIZATION_ENERGY</code> ionization energy for all methods<br><code>ELECTRON_AFFINITY</code> electron affinity for all methods<br><code>HOMO_ENERGY</code> HOMO energy from DFHF calculations<br><code>LUMO_ENERGY</code> LUMO energy from DFHF calculations<br><code>QM9_ID</code> ID of the molecule in the QM9 dataset</p> <p><strong>QM9IPEA_atom_ens.json</strong></p> <p><code>SPINS</code> the spin assigned to elements during calculations of atomic energies<br><code>ENERGY</code> energies of atoms using different methods</p> <p> </p> <p> </p> <p>All energies are given in Hartrees with NaN indicating the calculation failed to converge. Ionization potentials and electron affinities can be recovered as energy differences between neutral and charged (+1 for ionization potentials, -1 for electron affinities) species.</p> <p>"CPU_time" entries contain steps corresponding to individual method calculations, as well as steps corresponding to program operation: "INT" (calculating integrals over basis functions relevant for the calculation), "FILE" (dumping intermediate data to restart file), and "RESTART" (importing restart data). The latter two steps appeared since we reused relevant integrals calculated for neutral species in charged species' calculations; we also used restart functionality to use HF density matrix obtained for the neutral species as the initial density matrix guess for the SCF-HF calculation for charged species. NaN CPU time value means the step was not present or that the calculation is invalid. Note that the CPU times were measured while parallelizing on 12 cores and were not adjusted to single-core.</p> <p><strong> </strong></p> <p><strong><em>QM9IPEA_atom_ens.json</em></strong> contains atomic energies used to calculate atomization energies in <strong><em>QM9IPEA.json</em></strong>, the dictionary keys are:</p> <p><code>SPINS</code> - the spin assigned to elements during calculations of atomic energies.</p> <p><code>ENERGY</code> - energies of atoms using different methods.</p> <p> </p> <p>(Note that H has only one electron and thus does not require a level of theory beyond Hartree-Fock.)</p> <p>NOTE: Additional calculations were performed between publication of arXiv:2308.11196 and creation of this upload. For the version of the dataset used in the manuscript, please refer to DOI:10.5281/zenodo.8252498.</p> <h3>Acknowledgement</h3> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957189 (BIG-MAP) and No. 957213 (BATTERY 2030+). O.A.v.L. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772834). O.A.v.L. has received support as the Ed Clark Chair of Advanced Materials and as a Canada CIFAR AI Chair. O.A.v.L. acknowledges that this research is part of the University of Toronto’s Acceleration Consortium, which receives funding from the Canada First Research Excellence Fund (CFREF). Obtaining the presented computational results has been facilitated using the queueing system implemented at <a href="https://leruli.com">https://leruli.com</a>. The project has been supported by the Swedish Research Council (Vetenskapsrådet), and the Swedish National Strategic e-Science program eSSENCE as well as by computing resources from the Swedish National Infrastructure for Computing (SNIC/NAISS).</p> <p> </p> <h3>References</h3> <p>[1] Klamt, A.; Eckert, F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria 2000, 172, 43–72</p> <p>[2] Werner, H.-J.; Knowles, P. J.; Knizia, G.; Manby, F. R.; Schutz, M. Molpro: a general-purpose quantum chemistry program package. WIREs Comput. Mol. Sci. 2012, 2, 242–253</p> <p>[3] arxiv link of draft</p> <p>[4] <a href="https://github.com/chemspacelab/ViennaUppDa">https://github.com/chemspacelab/ViennaUppDa</a></p> <p>[5] Ramakrishnan, R.; Dral, P. O.; Rupp, M.; von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 2014, 1, 140022</p> <p>[6] Qu, X.; Jain, A.; Rajput, N. N.; Cheng, L.; Zhang, Y.; Ong, S. P.; Brafman, M.; Mag- inn, E.; Curtiss, L. A.; Persson, K. A. The Electrolyte Genome Project: A big data approach in battery materials discovery. Comput. Mater. Sci. 2015, 103, 56–67</p> <p><strong> </strong>[7] Ruddigkeit, L.; van Deursen, R.; Blum, L. C.; Reymond, J.-L. Enu- meration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17. Journal of Chemical Information and Modeling 2012, 52, 2864–2875</p> <p>[8] Irwin, J. J.; Shoichet, B. K. ZINC A Free Database of Commercially Available Compounds for Virtual Screening. Journal of Chemical Information and Modeling 2005, 45, 177–182.</p>
Trajectories used for Tailoring Charge Transfer Kinetics in Organic Radical Batteries
<p>Trajectories, which are discussed in our work "Tailored Charge Transfer Kinetics in Organic Radical Batteries - A Joint Synthetic-Theoretical Approach"</p> <p>The trajectories were obtained by linearly interpolating in internal coordinates (LIICs) of the relaxed ground state species of molecules A to F, where the charge is either localized on the thiophene backbone (B) or the TEMPO moiety (T1 and T2). Thereby, the program suite pysisyphus (Steinmetzer <em>et. al.</em> 2021) was used to obtain the LIICs. Endpoints represent the fully optimized redox species. The trajectories were used to calculate the intramolecular charge transfer reactions.</p> <p>Trajectories of the following charge transfer reactions are uploaded:</p> <p>1. A<sub>B</sub>→A<sub>T1</sub> (ABtAT1.trj)</p> <p>2. A<sub>B</sub>→A<sub>T2 </sub>(ABtAT2.trj)</p> <p>3. B<sub>B</sub>→B<sub>T1 </sub>(BBtBT1.trj)</p> <p>4. B<sub>B</sub>→B<sub>T2 </sub>(BBtBT2.trj)</p> <p>5. C<sub>B</sub>→C<sub>T1 </sub>(CBtCT1.trj)</p> <p>6. C<sub>B</sub>→C<sub>T2 </sub>(CBtCT2.trj)</p> <p>7. D<sub>B</sub>→D<sub>T1 </sub>(DBtDT1.trj)</p> <p>8. D<sub>B</sub>→D<sub>T2 </sub>(DBtDT2.trj)</p> <p>9. E<sub>B</sub>→E<sub>T </sub>(EBtET.trj)</p> <p>10. F<sub>B</sub>→F<sub>T </sub>(FBtFT.trj)</p>
Datasets : Unprecedented Aqueous Solubility of TEMPO and its Application as High Capacity Catholyte for Aqueous Organic Redox Flow Batteries
<p>Dataset of publication DOI: 10.1002/aenm.202301929 published in Advanced Energy Materials journal</p> <p>Despite the excellent electrochemical properties of non-functionalized 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO), its use in aqueous organic redox flow battery (AORFB) is hindered to date due to its insolubility in water. However, in this study, an unprecedented solubility of 5.6 M is demonstrated in an aqueous solution of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), which is 80 times higher than in water (0.07 M). A computational study reveals that the unique interaction between TEMPO and TFSI is essential to achieve this record solubility. TEMPO catholytes are tested in symmetric flow cells, demonstrating high capacity (23.85 AhL<sup>-1</sup>), high material utilization (89%), and robust reversible performance with long-term stability (low capacity fading of 0.082%/day). When paired with sulfonated viologen anolyte ((SPr2)V), an AORFB with low capacity fading over cycling (0.60%/day, 0.048%/cycle) is achieved, constituting the first example of a non-functionalized TEMPO catholyte for AORFB. Notably, this solubilization strategy could be applied to other unexplored chemistries in aqueous electrolytes, leading to the development of new AORFBs</p>
Practical Cell Design for PTMA-Based Organic Batteries: an Experimental and Modeling Study - Supporting Dataset
<p>The cycling and impedance data used in the work "Practical Cell Design for PTMA-Based Organic Batteries: an Experimental and Modeling Study" are provided in this repository.<br> The data are reported both as original .txt files with the raw data from the instruments and as processed Matlab files, where the data are structured in cycles.<br> The Excel sheet "Metadata" explains the type of battery associated to each code and file(s).</p> <p><br> </p>
Dataset for On the Tunability of Toxicity for Viologen-Derivatives as Anolyte for Neutral Aqueous Organic Redox Flow Batteries
<p>Dataset of the results published in ChemSusChem 10.1002/cssc.202300626. Viologen-derivatives are the most widely used redox organic molecules for neutral pH negative electrolyte of redox flow batteries. However, the long-established toxicity of the herbicide methyl-viologen raises concern for deployment of viologen-derivatives at large scale in flow batteries. Herein, we demonstrate the radically different cytotoxicity and toxicology of a series of viologen-derivatives in in vitro assays using model organisms representative of human and environmental exposure, namely human lung carcinoma epithelial cell line (A549) and the yeast Saccharomyces cerevisiae. The results show that safe viologen derivatives can be molecularly engineered, representing a promising family of negolyte materials for neutral redox flow batteries</p>
π-Conjugated Metal Free Porphyrin as Organic Cathode for Aluminum Batteries
<p>A metal-free porphyrin (TDPP) with a diphenylamino-Phenyl group is proposed as an electrode for aluminum-based batteries. The electrochemical performance is explored thoroughly. The extended porphyrin conjugated structure by the diphenylamino-phenyl groups shows a reversible capacity of 83 mAh.g-1 at 1 A. g-1 after 200 cycles and good cycling stability.</p>
Metadata - Tetrathiafulvalene-based covalent organic framework as high-voltage organic cathodes for lithium batteries
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Dataset of article : A phenazine-based conjugated microporous polymer as high performing cathode for aluminium-organic batteries
<p>Data used for preparation of the article : A phenazine-based conjugated microporous polymer as high performing cathode for aluminium-organic batteries</p><p> Abstract of article: </p><p>Here, we present one of the first examples of a phenazine-based hybrid microporous polymer, referred to as IEP-27-SR, utilized as an organic cathode in an aluminium battery with an AlCl3-EMIMCl ionic liquid electrolyte. The preliminary redox and charge storage mechanism of IEP-27-SR was confirmed by ex situ ATR-IR and EDS analyses. The introduction of phenazine active units in a robust microporous framework resulted in a remarkable rate-capability (specific capacity of 116 mAh g-1 at 0.5C with 77% capacity retention at 10C) and notable cycling stability, maintaining 75% of their initial capacity after 3440 charge-discharge cycles at 1C (127 days of continuous cycling). This superior performance compared to reported Al//n-type organic cathode RABs is attributed to the stable 3D porous microstructure and the presence of micro/mesoporosity of IEP-27-SR, which facilitates electrolyte permeability and improves kinetics.</p>
Dataset for the paper "Aqueous Redox Flow Batteries: Small Organic Molecules for the Positive Electrolyte Species", ChemSusChem, DOI: 10.1002/cssc.202300303
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Authors:Christopher G. Cannon, Dr. Peter A. A. Klusener, Prof. Nigel P. Brandon, Prof. Anthony R. J. Kucernak</p> <p>Title:Aqueous Redox Flow Batteries: Small Organic Molecules for the Positive Electrolyte Species</p> <p>Journal:ChemSusChem</p> <p>DOI:10.1002/cssc.202300303</p> <p>Please cite the above reference if you wish to use this data</p>
Metadata - Tuning the electrochemical performance of covalent organic framework cathodes for Li- and Mg-based batteries: the influence of electrolyte and binder
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Unlocking twofold oxidation in phenothiazine polymers for application in symmetric all-organic anionic batteries
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Raw Data files for publication: " A matter of design and coupling: High indoor charging efficiencies with organic solar modules direct coupled to a sodium ion battery"
<p>Supporting raw data for the publication linked below: </p> <p>Inclusive of</p> <ul> <li>spectral data of light sources </li> <li>LED calibration data </li> <li>PV characterization data under LED and AM1.5 </li> <li>battery characterization data and charge-discharge data</li> </ul>
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