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Item Anhang für "Selbstassoziierende frustrierte Lewis-Paare und mechanistische Untersuchungen zu Piers‘ Boran induzierten Umlagerungen und Carboborierungen"(2023-05-08) Müller, Tizian Jürgen; Justus-Liebig-Universität GießenRohdaten zur Dissertation „Selbstassoziierende frustrierte Lewis-Paare und mechanistische Untersuchungen zu Piers‘ Boran induzierten Umlagerungen und Carboborierungen“ vorgelegt von Tizian Jürgen Müller. Diese Sammlung enthält: - NMR-Spektren der charakterisierten Verbindungen und durchgeführten Kinetiken. - SC-XRC Rohdaten und cif-Dateien. - Auswertung der durchgeführten Kinetiken als xlsx-Datei - Rohdaten der diskutierten quantenmechanischen Rechnungen als out-Dateien.Item Data and Code for "Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ∆∆G‡ versus enantiomeric excess"(2023-10-13) Ruth, Marcel; Institute of Organic Chemistry, Justus Liebig University; Institute of Chemistry, TU BerlinThis repository contains all datasets that were used to evaluate the difference between ee and ΔΔG‡ modeling in enantioselective organocatalytic reactions. The scripts and notebooks used are also included to elucidate our modeling process. All descriptor and fingerprint-based models are included in "descriptorbased_parametric_models-repeated.ipynb". The evaluations and hyperparameter optimizations by our graph neural network are split into several small scripts and helper functions (basically all Python files). Article abstract: The modeling of catalytic, enantioselective reactions is pivotal for chiral drug development, green chemistry, and industrial applications. While ligand-based and quantitative structure-activity relationships have a long history, the limitations of these methods, including inadequate representation of reaction dynamics and physical constraints, have become increasingly evident. With the rise of machine learning due to increased computational power, the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict reactions. Here we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using instead physically grounded differences Gibbs free activation energies (ΔΔG‡). We outline key benefits, such as enhanced modeling performance using ΔΔG‡, escaping physical limitations, addressing temperature effects, managing non-linear error propagation, adjusting for data distributions, and how to deal with unphysical predictions. For this endeavor, we gathered ten datasets from the literature covering very different reaction types, e.g., hydrogenation, Suzuki-, and Heck-reactions for 2761 data points. We evaluated fingerprint, descriptor, and graph neural network based models. Our results highlight the distinction in performance among varying model complexities and emphasize the importance of choosing suitable metrics for accurate and robust chemical modeling.Item Data and Code for "Designing the Next Better Catalyst Utilizing Machine Learning with a Key-Intermediate Graph: Differentiating a Methyl from an Ethyl Group"(2023-11-15) Pereira, Oliver; Ruth, Marcel# Dataset and Scripts Overview ## General Overview This dataset includes a series of Python scripts and Jupyter notebooks that are primarily focused on the analysis, modeling, and visualization of chemical data. The scripts encompass various aspects of data preprocessing, including graph-based transformations, model definitions for Graph Neural Networks (GNNs) and Feedforward Neural Networks (FFNNs), training utilities like early stopping, and comprehensive workflows for training, evaluating, and visualizing model performance. ## File Descriptions ### Python Scripts 1. **CV_cat-subs.py**: Script for training and evaluating a machine learning model using cross-validation. 2. **LOOCV_CV.py**: Similar to CV_cat-subs.py but employs Leave-One-Out Cross-Validation for model evaluation. 3. **config.py**: Configuration file containing optimal parameters for the models. 4. **eval_mol_representation.py**: Evaluates molecular representations using various machine learning models. 5. **hpo_cbs.py**: Hyperparameter optimization script for tuning Graph Neural Network models. 6. **models.py**: Defines neural network models, including Graph Neural Networks. 7. **preprocessing_data_new.py**: Preprocesses the dataset, preparing it for analysis and modeling. 8. **preprocessing_graph_new.py**: Prepares graph-based data representations, essential for GNNs. 9. **screening_models.py**: Provides additional model definitions for various machine learning tasks. 10. **training.py**: Contains utilities for model training, including an early stopping mechanism. ### Jupyter Notebooks 1. **CV_plots.ipynb**: Focuses on data visualization, particularly for cross-validation results. 2. **main.ipynb**: A comprehensive notebook covering data preprocessing, model training, evaluation, and visualization. ## Dataset Structure: "CBS_10-04-2023.csv" The dataset "CBS_10-04-2023.csv" is a key component of this collection. It includes various chemical properties and molecular structures relevant to the domain of cheminformatics. The structure of the dataset comprises columns that detail different chemical entities (catalyst, substrate, product), reaction conditions, and results. This dataset is used extensively throughout the scripts and notebooks for preprocessing, analysis, and model training. Understanding its structure is crucial for interpreting the results and for any further modification or analysis. ## Usage Instructions To use these scripts and notebooks, ensure you have Python installed along with necessary libraries like Pandas, NumPy, Torch, Torch Geometric, and RDKit. Each script can be executed independently, provided the required data files are available in the specified paths. The Jupyter notebooks can be run in a sequence for a complete end-to-end workflow. ## Variable and Function Explanations - Variables and functions within the scripts and notebooks are named to reflect their purpose in data processing, modeling, or visualization tasks. Specific domain-related variables, such as those handling chemical properties or molecular structures, are used in accordance with standard practices in cheminformatics. ## Additional Notes - These scripts and notebooks are tailored for chemical data analysis and may require domain-specific understanding for optimal usage and interpretation of results.Item Data and Code for "Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies"(2023-02-02) Ruth, Marcel; Institute of Organic ChemistryThe datasets, models, and scripts were created to achieve an accurate prediction of the increment of single-point energies between density functional theory (DFT) and wavefunction-based methods, which led to our submitted article: 'A Machine Learning Approach for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies'. We used the ORCA quantum chemical package to compute the geometries of each species at the B3LYP-D3(BJ)/cc-pVTZ level of theory. The optimized structure was subsequently employed for single-point (SP) computations at the DLPNO-CCSD(T)/cc-pVTZ and CCSD(T)/cc-pVTZ levels of theory. All data were extracted from the calculations and compiled in the provided .csv files. With the datasets and prediction scripts, it is possible to forecast the differences in single-point (SP) energies between the B3LYP-D3(BJ)/cc-pVTZ and DLPNO-CCSD(T)/cc-pVTZ (for monomers and dimers) levels of theory, as well as to the CCSD(T)/cc-pVTZ level of theory for monomers. The datasets can be opened and read with any text editor. The Pytorch models can be loaded and manipulated as usual (https://pytorch.org/tutorials/beginner/saving_loading_models.html). The prediction can be made by installing a suitable Python environment and setting the code line: test_database = f'TestDatabase_{mode}.csv' to the desired dataset for prediction. The format and column names of the file should match the uploaded dataset files. Once the line is modified, a prediction can be generated using the following command, for example, “python gen_predictions_CCSDt.py”.Item Data for "1,1,2-Ethenetriol: The Enol of Glycolic Acid, a High-Energy Prebiotic Molecule"(2021) Schreiner, PeterRaw IR , UV/is and NMR spectra for the publication A. Mardyukov, F. Keul, and P. R. Schreiner "1,1,2-Ethenetriol: The Enol of Glycolic Acid, a High-Energy Prebiotic Molecule" . File format is the Bruker vibrational spectroscopy software OPUS for visualization, processing, analysis IR and UV/Vis data. File format is MestReNova NMR software for visualization, processing, analysis NMR data.Item Data for "Development of a multi‐step screening procedure for redox active molecules in organic radical polymer anodes and as redox flow anolytes"(2024-01) Achazi, Andreas J.; Mollenhauer, DoreenCalculated structural data of organic redox compounds for the publication: Andreas J. Achazi, Xhesilda Fataj, Philip Rohland, Martin D. Hager, Ulrich S. Schubert, Doreen Mollenhauer. "Development of a multi‐step screening procedure for redox active molecules in organic radical polymer anodes and as redox flow anolytes". The structures were optimized at different levels of theory. After downloading and unpacking the *zip-file you will find a file named "Readme.pdf" which contains all details on how to use the dataset and additional computational information to recalculate all data from the dataset (information about software etc.).Item Data for "Development of novel redox-active organic materials based on benzimidazole, benzoxazole, and benzothiazole – a combined theoretical and experimental screening approach"(2023-12) Achazi, Andreas J.; Pohl, K. Linus H.; Mollenhauer, DoreenCalculated structural data of organic redox moieties for the publication: Xhesilda Fataj, Andreas J. Achazi, Philip Rohland, Erik Schröter, Simon Muench, René Burges, K. Linus H. Pohl, Doreen Mollenhauer, Martin D. Hager, Ulrich S. Schubert. "Development of Novel Redox-Active Organic Materials Based on Benzimidazole, Benzoxazole, and Benzothiazole: A Combined Theoretical and Experimental Screening Approach". All structures were optimized at the BP86D3(BJ)/def2-TZVP (DCOSMO-RS) level of theory. The file format is xyz. In the folders are usually subfolders. "n" stands for neutral molecules, "p" stands for plus which means the molecules have a charge of +1, "pp" stands for plusplus which means the molecules have a charge of +2, and "m" stands for minus which means the molecules have a charge of -1. The rotated folder has only the molecules with a charge of +1. It contains the structures calculated with COSMO and in that folder with DCOSMO-RS. The number in front stands for the degree of rotation. You can find the same information in the ReadMe-file included in the zip-archive.Item Data for "Insight into the Li/LiPON Interface at the Molecular Level: Interfacial Decomposition and Reconfiguration"(2024-05) Wang, Kangli; Mollenhauer, DoreenThis dataset contains the calculated structures involving the surface and interface for the publication: Kangli Wang, Jürgen Janek, Doreen Mollenhauer. "Insight into the Li/LiPON Interface at the Molecular Level: Interfacial Decomposition and Reconfiguration". Further information relevant to the reuse of the dataset can be found in the included readme file.Item Data for "Matrix Isolation and Photorearrangement of Cis- and Trans- 1,2-Ethenediol to Glycolaldehyde"(2022) Mardyukov, ArturRaw IR , UV/is and NMR spectra for the publication A. Mardyukov, R. C. Wende, and P. R. Schreiner "Matrix Isolation and Photorearrangement of Cis- and Trans- 1,2-Ethenediol to Glycolaldehyde". File format is the Bruker vibrational spectroscopy software OPUS for visualization, processing and analysis of IR and UV/Vis data. File format is MestReNova NMR software for visualization, processing and analysis of NMR data.Item Data for "The Enol of Isobutyric Acid"(2024) Danho, AkkadSpectra for deuterated and non-deuterated enols, along with density functional theory computations IR and UVVis-spectra. A Sumitomo cryostat system consisting of an RDK 408D2 closed-cycle refrigerator cold head and an F-70 compressor unit was used for matrix isolation experiments. A polished CsI window was mounted in the cold head sample holder. The sample holder, connected with silicon diodes for temperature measurements, was covered by a vacuum shroud, which was equipped with KBr windows to allow for IR measurements. In some experiments BaF2 windows were used due to their higher transparency when measuring UV/vis spectra. The sample and the host gas (Ar, purity of 99.999%) were co-deposited at 3.5 K. All spectral data were collected at 3.5 K. The pyrolysis zone was equipped with a heatable 90 mm long quartz tube (inner diameter 7 mm), controlled by a Ni/CrNi thermocouple. The travel distance of the sample from the pyrolysis zone to the matrix was ∼45 mm. Ar was stored in a 2 L gas balloon, which was evacuated and filled three times before every experiment. The sample was evaporated from a Schlenk tube at 80 °C (water) and reduced pressure (∼3 × 10–6 mbar) and co-deposited with a high excess of argon on both sides of the matrix window in the dark (preventing unwanted photochemistry) at a rate of ∼1 mbar min–1, based on the pressure inside the Ar balloon. Pyrolyses were carried out at 750 °C. IR spectra were recorded between 7000 and 350 cm–1 with a resolution of 0.7 cm–1 with a Bruker Vertex 70 FTIR spectrometer. A spectrum of the cold matrix window before deposition was used as background spectrum for the subsequent IR measurements. UV/vis spectra were recorded between 190 and 800 nm with a resolution of 1 nm with a Jasco V-760 spectrophotometer. A high-pressure-mercury lamp equipped with a monochromator (LOT Quantum Design) or a low-pressure-mercury lamp (Gräntzel) fitted with a Vycor filter were used for irradiation of the matrix during photochemical experiments. Spectra were saved as "X-files" and can be opened with "OPUS". Computations. All DFT computations were performed with the Gaussian 16,1 Revision C.01 (full citations for electronic structure codes are given at the end of this document) at the B3LYP/def2-TZVP2-3 level of theory. The keywords Opt and Freq=NoRaman were used for the characterization of minima on the PES. For transition structures the keyword Opt=(ts,tight,calcfc,noeigen) was used. UV/Vis absorptions were computed by using the keyword td(50-50,nstates=10). The results of the calculations were saved as "out" files and can be opened with the editor and graphically with 'ChemCraft'.Item Data for "The enol of propionic acid"(2023-08-25) Danho, AkkadIR and UVVis-spectra. A Sumitomo cryostat system consisting of an RDK 408D2 closed-cycle refrigerator cold head and an F-70 compressor unit was used for matrix isolation experiments. A polished CsI window was mounted in the cold head sample holder. The sample holder, connected with silicon diodes for temperature measurements, was covered by a vacuum shroud, which was equipped with KBr windows to allow for IR measurements. In some experiments BaF2 windows were used due to their higher transparency when measuring UV/vis spectra. The sample and the host gas (Ar, purity of 99.999%) were co-deposited at 3.5 K. All spectral data were collected at 3.5 K. The pyrolysis zone was equipped with a heatable 90 mm long quartz tube (inner diameter 7 mm), controlled by a Ni/CrNi thermocouple. The travel distance of the sample from the pyrolysis zone to the matrix was ∼45 mm. Ar was stored in a 2 L gas balloon, which was evacuated and filled three times before every experiment. The sample was evaporated from a Schlenk tube at 70 °C (water) and reduced pressure (∼3 × 10–6 mbar) and co-deposited with a high excess of argon on both sides of the matrix window in the dark (preventing unwanted photochemistry) at a rate of ∼1 mbar min–1, based on the pressure inside the Ar balloon. Pyrolyses were carried out at 500 °C. IR spectra were recorded between 7000 and 350 cm–1 with a resolution of 0.7 cm–1 with a Bruker Vertex 70 FTIR spectrometer. A spectrum of the cold matrix window before deposition was used as background spectrum for the subsequent IR measurements. UV/vis spectra were recorded between 190 and 800 nm with a resolution of 1 nm with a Jasco V-760 spectrophotometer. A high-pressure-mercury lamp equipped with a monochromator (LOT Quantum Design) or a low-pressure-mercury lamp (Gräntzel) fitted with a Vycor filter were used for irradiation of the matrix during photochemical experiments. Spectra were saved as "X-files" and can be opened with "OPUS". Computations. All DFT computations were performed with the Gaussian 16,1 Revision C.01 (full citations for electronic structure codes are given at the end of this document) at the B3LYP/def2-TZVP2-3 level of theory. The keywords Opt and Freq=NoRaman were used for the characterization of minima on the PES. For transition structures the keyword Opt=(ts,tight,calcfc,noeigen) was used. UV/Vis absorptions were computed by using the keyword td(50-50,nstates=10). The results of the calculations were saved as "out" files and can be opened with the editor and graphically with 'ChemCraft'.Item Data for "The Intrinsic Barrier Width and Its Role in Chemical Reactivity"(2023-03-07) Qiu, Guanqi; Institute of Organic ChemistryRaw IR spectra for the publication G. Qiu and P. R. Schreiner "The Intrinsic Barrier Width and its Role in Chemical Reactivity". File format is the Bruker vibrational spectroscopy software OPUS and MestReNova for visualization, processing, and analysis of IR data. The integrals of the relevant peaks were collected in the Excel file. Geometry optimizations and non-projected IRCs were computed at the MP2/cc-pVDZ level of theory using the Gaussian 16 package. All output files are provided. Tunneling half-lives and projected IRCs were determined at the CVT/SCT//MP2/cc-pVDZ level of theory using Polyrate Version 2017-c. Both the Polyrate code and the output files are provided.Item Electrochemical data for "Enhancing the Electrochemical Performance of LiNi0.70Co0.15Mn0.15O2 Cathodes Using a Practical Solution-Based Al2O3 Coating"(2020-06-05) Negi, Rajendra S.Raw cycling data and electrochemical impedance spectroscopy (EIS) data of the cells investigated in the publication Negi, R.S, Culver, S.P., Mazilkin, A., Brezesinski, T., Elm, M.T. “Enhancing the Electrochemical Performance of LiNi0.70Co0.15Mn0.15O2Cathodes Using a Practical Solution-Based Al2O3Coating” ACS Appl. Mater. Interfaces 2020, 12, 31392-31400; https://doi.org/10.1021/acsami.0c06484. File format is the standard .txt format. Cycling data of the three pristine (uncoated) NCM cathodes (P-NCM) and the three investigated, coated NCM cathodes (C-NCM) are stored in three blocks: 1. Cycle number, 2. Discharge capacity cell1 (mAh g-1), 3. Discharge capacity cell2 (mAh g-1), 4. Discharge capacity cell3 (mAh g-1). Experimental impedance data after the first cycle and the 100th cycle for one pristine (P-NCM) and coated cathode (C-NCM) are stored in 3 blocks: 1. Frequency (Hz), 2. Real part (Ohm), 3. Imaginary Part (Ohm). In each file, blocks 4 and 5 give the results of the fitting according to the equivalent circuit discussed in the publication. Block 4: Fitted real part (Ohm), block 5: Fitted imaginary part (Ohm).Item Experimental Data for "Enhancing the Analysis of Eu3+ Photoluminescence in Coordination Compounds in the Solid State by Determining their Refractive Index"(2024) Sedykh, Alexander E.; Lehrstuhl für Chemische Technologie der Materialsynthese Julius-Maximilians-Universität Würzburg Röntgenring 11, 97070 Würzburg, GermanyNormalised excitation and emission spectra, UV-Vis reflectance spectra, powder X-ray diffraction data (Cu-Kalpha radiation), and thermal analysis data of compounds alpha-[Eu(NO3)3(ptpy)(H2O)] (1), beta-[Eu(NO3)3(ptpy)(H2O)] (2), [Eu(NO3)3(ptpy)(acetone)] (3), [Eu(NO3)3(ptpy)(thf)] (4), [Eu(NO3)3(ptpy)(MeOH)] (5), [EuCl3(ptpy)(acetone)] (6), [EuCl3(ptpy)(thf)] (7), and [EuCl3(ptpy)(MeOH)] (8). Files' designation: N_Ex: photoluminescence excitation spectrum of compound N (xy data). N_Em: photoluminescence emission spectrum of compound N (xy data). N_UVVis: UV-Vis reflectance spectrum of compound N (xy data). N_PXRD: powder X-ray diffraction data of compound N (xy data). N_STAMS: simultaneous thermogravimetry and differential thermal analysis coupled with mass-spectrometry (STA-MS) data of compound N (multi-y-column xy data). N_TPXRD: temperature-dependent powder X-ray diffraction data of compound N (multiple xy data).Item Organocatalytic, Chemoselective, and Stereospecific House-Meinwald Rearrangement of Trisubstituted Epoxides(2023) Dressler, FriedemannWe present a novel method for the chemoselective House-Meinwald rearrangement of trisubstituted epoxides under mild conditions with the use of simple perfluorinated disulfonimides as Brønsted acid catalysts. We isolated the α-quaternary aldehyde products in yields of 27-97% using catalyst loadings as low as 0.5 mol% on a scale of 1 mmol. In addition, we show the stereospecific rearrangement using an enantioenriched substrate, which makes this method suitable for applications in total synthesis of natural products. We provide free induction decay (FID) of nuclear magnetic resonance (NMR) spectra of new compounds obtained within the manuscript submitted for publication in Synlet (Thieme). The NMR spectra were recorded on Bruker AV 400 or AV 400HD spectrometers at 298 K (400 MHz for 1H NMR, 377 MHz for 19F NMR, and 101 MHz for 13C NMR). To open and read the FID files either use the programs "Mnova" (https://mestrelab.com/download/mnova/) or "Bruker TopSpin" (https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html).Item Polymorphism and White Light Emission of 1-Bromo-3,5,7-Triphenyladamantane compared with 1,3,5,7-Tetraphenyladamantane(2024) Saravanan, Gowrisankar; Fokin, Andrey A.; Becker, Jonathan; Mathew, Neeshma; Schmedt auf der Günne, Jörn; Schreiner, Peter RSurprisingly, the crystal structures of bromophenyl adamantanes finely depend on two flavors of London dispersion (LD) interactions: relatively strong CH−π and quite weak Br⋯Br interactions. Here we report our investigation of 1-bromo-3,5,7-triphenyladamantane (1, BrAdPh3) and elucidation of two polymorphic crystal structures denoted as 1A and 1B using single crystal X-ray diffraction (SCXRD). In the monoclinic crystal system of 1A (P21/n space group), we observed CH−π interactions, while Br···Br interactions were absent. Conversely, the Br···Br interactions are a structure-defining factor in the formation of the monoclinic crystal system of 1B (R3 ̅ space group). To provide context, we compare our findings with 1,3,5,7-tetraphenyladamantane (2), characterized by numerous CH−π interactions orchestrating the molecules into chains in the solid. Both CH (phenyl) and CH2 (adamantane) groups thereby participate as dispersion energy donors (DEDs). Computational analyses were employed to investigate the interactions within the characteristic dimers present in the unit cells of 1A and 1B, including visualization of noncovalent interactions and the use of the atoms-in-molecules approach, and molecular orbital analysis. These support the notion of LD dimer-dimer interactions in 1A between the phenyl moieties, whereas 1B exhibits additional dimer-dimer Br···Br contacts. In contrast, the crystals of 2 are exclusively held together by CH−π stacking LD interactions, a feature absent in the polymorphs of 1. Both polymorphic forms of 1 emit white light when subjected to 900 nm continuous wave laser irradiation, displaying a subtle blue shift compared to 2. The absence of CH−π stacking interactions between the dimers of 1 causes a small red-shift in the emission spectrum. The NMR, SCXRD and Computation files are included in this dataset.Item Preparation and spectroscopic identification of the cyclic carbon dioxide dimer 1,2-dioxetanedione(2023-06-19) Gerbig, DennisMatrix isolation spectra as ASCII data point files in simple xy-format. Files can be opened and plotted by any combination of text editor and plotting program, respectively. Spectra were recorded on a Bruker Invenio R infrared spectrometer in conjunction with the Bruker OPUS 8.5 SP1 software.Item Structure Data for "Increased molecular inhomogeneity and cluster glass formation as a basis for inexpensive white-light production"(2022-03-04) Schwan, Sebastian; Mollenhauer, DoreenCalculated structural data of monomers and dimers of cluster materials for the publication: Irán Rojas-León, Jan Christmann, Sebastian Schwan, Ferdinand Ziese, Simone Sanna, Doreen Mollenhauer, Nils W. Rosemann, Stefanie Dehnen, “Increased molecular inhomogeneity and cluster glass formation as a basis for inexpensive white-light production”. The file format is xyz.Item Synthesis and Functionalization of Isomeric Sesquihomodiamantenes(2023-08-16) Fokin, AndreyAnti- and syn-sesquihomodiamantenes (SDs) were prepared and structurally characterized. Anti-SD and parent sesquihomoadamantene (SA) were CH-bond functionalized utilizing a phase-transfer protocol. The DFT computed ionization potentials of unsaturated diamondoid dimers correlate well with the experimental oxidation potentials obtained from cyclic voltammetry. Similar geometries ensue for both the reduced and ionized SD states, whose persistence is supported by β-hydrogen’s spatial sheltering. This makes SDs promising building blocks for the construction of diamond materials with high stability and carrier mobility. We provide free induction decay (FID) of nuclear magnetic resonance (NMR) spectra of new compounds obtained within the manuscript accepted for publication in Journal of the Organic Chemistry (American Chemical Society). The NMR spectra were recorded in CDCl3 solutions on a Bruker AV 400 (400 MHz for 1H NMR and 100 MHz for 13C NMR), Bruker AV 600 (600 MHz for 1H NMR and 125 MHz for 13C NMR), Bruker Avance Neo (700 MHz for 1H NMR and 176 MHz for 13C NMR), and Bruker AV 850 (850 MHz for 1H NMR and 214 MHz for 13C NMR) spectrometers at 298 K. To open and read the FID files either use the programs "Mnova" (https://mestrelab.com/download/mnova/) or "Bruker TopSpin" (https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html).Item ToF-SIMS raw data for "Bridging the Gap: Electrode Microstructure and Interphase Characterization by Combining ToF-SIMS and Machine Learning"(2023-09) Lombardo, Teo; Kern, ChristineToF-SIMS raw data for the paper "Bridging the Gap: Electrode Microstructure and Interphase Characterization by Combining ToF-SIMS and Machine Learning". In the article accompanying the raw data published here, an analytical method is presented to analyze large battery electrode microstructures using time-of-flight secondary ion mass spectrometry (ToF-SIMS). This enables imaging of the spatial distribution of the main phases (e.g., active material, carbon-binder domain) and the degradation products formed during cycling (solid or cathode-electrolyte interphase). Focus is on imaging the microstructure and single particle/agglomerates of large 2D electrode cross sections. The article further demonstrates that this analysis can be extended to 3D electrode microstructures when ToF-SIMS and specific machine learning techniques are combined. File types of ToF-SIMS raw data (.itax, .itmx, and .itm) can be opened, read and evaluated with the SurfaceLab software of IONTOF GmbH company (Muenster, Germany). ToF-SIMS data are also exported to 2D imzML (pixel spectra) and txt files. In the following each file is listed with the respective description to it: - Cycled_graphite_9014_DE_2048_250_after SE cleaning_1.itax --> .itax file used for Figure 1 (cycled graphite electrode) - cycled_graphite_9014_de_2048_250_after se cleaning_1.txt --> .txt file used for Figure 1 (cycled graphite electrode) - Cycled_graphite_9014_DE_2048_250_after SE cleaning_1_itmx.7z --> zipped .itmx file used for Figure 1 (cycled graphite electrode) - Cycled_graphite_9014_DE_2048_250_after SE cleaning_1_ImzML.7z --> zipped .ImzML file used for Figure 1 (cycled graphite electrode) - Cycled_graphite_9014_DE_2048_250_after SE cleaning_1_ibd.7z --> zipped .ibd file used for Figure 1 (cycled graphite electrode) - Cycled_graphite_9014_DE_2048_250_after SE cleaning_1.zip.001 to 004 --> in 4 parts-zipped .itm file used for Figure 1 (cycled graphite electrode) - Graphite_cycled_9014_cathode_DE_250x250µm²_2048_after FIB and SE.itax --> .itax file used for Figure 4, Figures S6-S8 (file was named wrong, it is an analysis of an NMC electrode) - graphite_cycled_9014_cathode_de_250x250µm²_2048_after fib and se_.txt --> .txt file used for Figure 4, Figures S6-S8 (file was named wrong, it is an analysis of an NMC electrode) - Graphite_cycled_9014_cathode_DE_250x250µm²_2048_after FIB and SE_itmx.7z --> zipped .itmx file used for Figure 4, Figures S6-S8 (file was named wrong, it is an analysis of an NMC electrode) - Graphite_cycled_9014_cathode_DE_250x250µm²_2048_after FIB and SE.ImzML --> .ImzML file used for Figure 4, Figures S6-S8 (file was named wrong, it is an analysis of an NMC electrode) - Graphite_cycled_9014_cathode_DE_250x250µm²_2048_after FIB and SE_ibd.7z --> zipped .ibd file used for Figure 4, Figures S6-S8 (file was named wrong, it is an analysis of an NMC electrode) - Graphite_cycled_9014_cathode_DE_250x250µm²_2048_after FIB and SE.zip.001 to 006 --> in 6 parts zipped .itm file used for Figure 4, Figures S6-S8 (file was named wrong, it is an analysis of an NMC electrode) - Pre_30_Ref_DE_250_2040_5shots_1frame_FIBlong_FIBpolish_1.itax --> .itax file used for Figure 3, Figure 5, Figure S5 (prelithiated reference electrode) - pre_30_ref_de_250_2040_5shots_1frame_fiblong_fibpolish_1.txt --> .txt file used for Figure 3, Figure 5, Figure S5 (prelithiated reference electrode) - Pre_30_Ref_DE_250_2040_5shots_1frame_FIBlong_FIBpolish_1_itmx.7z --> zipped .itmx file used for Figure 3, Figure 5, Figure S5 (prelithiated reference electrode) - Pre_30_Ref_DE_250_2040_5shots_1frame_FIBlong_FIBpolish_1_lmzML.7z --> zipped .ImzML file used for Figure 3, Figure 5, Figure S5 (prelithiated reference electrode) - Pre_30_Ref_DE_250_2040_5shots_1frame_FIBlong_FIBpolish_1_ibd.7z --> zipped .ibd file used for Figure 3, Figure 5, Figure S5 (prelithiated reference electrode) - Pre_30_Ref_DE_250_2040_5shots_1frame_FIBlong_FIBpolish_1.zip.001 to 007 --> in 7 parts zipped .itm file used for Figure 3, Figure 5, Figure S5 (prelithiated reference electrode) - Gr_HC_Si_one layer_negative_DE_2048pixels_rotated_after FIB_2.itax --> itax file used for Figure 2, Figures S2-S5 (graphite-silicon electrode) - gr_hc_si_one layer_negative_de_2048pixels_rotated_after fib_2.txt --> .txt file used for Figure 2, Figures S2-S5 (graphite silicon electrode) - Gr_HC_Si_one layer_negative_DE_2048pixels_rotated_after FIB_2.itmx --> itmx file used for Figure 2, Figures S2-S5 (graphite silicon electrode) - Gr_HC_Si_one layer_negative_DE_2048pixels_rotated_after FIB_2_ImzML.7z --> zipped ImzML file used for Figure 2, Figures S2-S5 (graphite silicon electrode) - Gr_HC_Si_one layer_negative_DE_2048pixels_rotated_after FIB_2.ibd --> ibd file used for Figure 2, Figures S2-S5 (graphite silicon electrode) - Gr_HC_Si_one layer_negative_DE_2048pixels_rotated_after FIB_2.zip.001 to 004 --> in 4 parts-zipped .itm file used for Figure 2, Figures S2-S5 (graphite silicon electrode)