Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
23
datasets available to search
ShareScore release 0.9.0
Dataset results
23 results for “quantum machine learning”
Hybrid quantum-classical machine learning for generative chemistry and drug design: Generated molecules
<p>Deep generative chemistry models emerge as powerful tools to expedite drug discovery. How- ever, the immense size and complexity of the structural space of all possible drug-like molecules pose significant obstacles, which could be overcome with hybrid architectures combining quantum computers with deep classical networks. As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. The size of the proposed model was small enough to fit on a state-of-the-art D-Wave quantum annealer and allowed training on a subset of the ChEMBL dataset of biologically active compounds. Finally, we generated 2331 novel chemical structures with medicinal chemistry and synthetic accessibility properties in the ranges typical for molecules from ChEMBL. The pre- sented results demonstrate the feasibility of using already existing or soon-to-be-available quantum computing devices as testbeds for future drug discovery applications.</p>
Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning"
<p>Dataset of experimental measurements for "Demonstration of quantum advantage in machine learning", <em>npj Quantum Information</em><strong> 3</strong>, Article number: 16 (2017).</p>
Machine Learning Quantum Reaction Rate Constants
<p>Dataset of 1,517,419 quantum reaction rate constant products <span class="math-tex">\(k^{\text {QM}}(T)Q_{\text R}(T)\)</span> computed from the transmission coefficient for model single and double barrier minimum energy paths. Here <span class="math-tex">\(k^{QM}(T) \)</span> is the quantum reaction rate constant at temperature <span class="math-tex">\(T\)</span> and <span class="math-tex">\(Q_\text{R}(T)\)</span> is the reactant partition function computed with the rigid rotor and harmonic oscillator approximations.This dataset was created for Ref [1] where it was used to train and test a DNN to predict <span class="math-tex">\(\log{k^{\text{QM}}(T)Q_\text{R}(T)}\)</span>.</p> <p><strong>Cite as</strong></p> <p>Please cite the following references when using this dataset:</p> <p>[1] E. Komp and S. Valleau, Machine Learning Quantum Reaction Rate Constants, <em>J. Phys. Chem. A</em>, 124:8607–8613, 2020, <a href="https://pubs.acs.org/doi/abs/10.1021/acs.jpca.0c05992">doi: 10.1021/acs.jpca.0c05992</a>.</p> <p>[2] E. Komp and S.Valleau, Machine Learning Quantum Reaction Rate Constants (1.0.0) [Data set], 2020, <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.5510392">https://doi.org/10.5281/zenodo.5510392 </a></p> <p><strong>Contents</strong></p> <p>Descriptions of entries in the tabular dataset file `QM_kQ.csv`. Please refer to the publication [1] for details.</p> <ul> <li>`mass_au`: Mass of the reactants in atomic units. </li> <li>`width_1_au`: Width of first potential energy barrier in atomic units.</li> <li>`width_2_au`: Width of second (if present) potential barrier in atomic units. For single barriers width_2_au = 0.0.</li> <li>`height_1_au`: Activation energy of first potential energy barrier in atomic units.</li> <li>`height_2_au`: Activation energy of second (if present) potential energy barrier in atomic units. For single barriers height_2_au = 0.0.</li> <li>`dist_au`: For double barriers, absolute value of the difference between the position of the two potential energy maxima along the reaction coordinate in atomic units. For single barriers dist_au = 0.0.</li> <li>`alpha_symm`: Symmetry constant for single barriers, defined as the difference between product and reactant energies in atomic units.</li> <li>`alpha_double`: Symmetry constant for double barriers, defined as the sum of the normalized difference between barrier heights and the normalized difference between barrier widths, unitless.</li> <li>`slope`: Slope of the first reaction barrier along the reaction coordinate in atomic units. Slope values were evaluated numerically from the type of potential energy barrier see Ref [1] SI.</li> <li>`temp_K`: Temperature in Kelvin.</li> <li>`kQ_rate`: Quantum reaction rate constant times reactant partition function in units of 1/ps. </li> <li>`log_kQ_rate`: Natural logarithm of the quantum reaction rate constant times the reactant partition function in units of log(1/ps).</li> </ul>
Data for: Explainable Quantum Machine Learning
<p>Data used in the numerical experiments for the publication "Explainable Quantum Machine Learning" (<a href="https://arxiv.org/abs/2301.09138">arXiv:2301.09138</a>).</p>
Machine learning the quantum flux-flux correlation function for catalytic surface reactions
<p>This dataset contains information on each of the 14 reactions used in the paper, the geometries for these reactions, the product of the quantum reaction rate constant and canonical reactant partition function and the flux-flux correlation function time series values for each reaction-temperature combination.</p> <p><strong>reaction_details.csv</strong></p> <p>This is a .csv file containing additional details on the reactions used in this paper. Each row contains one reaction/temperature combination, of which there are 55.</p> <p> </p> <p>Column descriptions:</p> <ul> <li>reaction_number: Reaction identifier number used in this work</li> <li>reaction: The chemical reaction equation</li> <li>metal_surface: atomic symbol of metal surface</li> <li>facet_number: Miller indices of surface</li> <li>reactants: Python dictionary object of reactants and their quantities</li> <li>products: Python dictionary object of products and their quantities</li> <li>reaction_energy [eV]: reaction energy in electron-volts</li> <li>activation_energy [eV]: activation energy of reaction in electron-volts</li> <li>temperature [K]: The randomly assigned temperature a calculation was run for</li> <li>kQ_Cff [1/au]: The calculated integrated reaction rate product at corresponding temperature {1,2,3,4} in units 1/(au time).</li> <li>reaction_split: Train/test placement of that reaction/temperature combination for reaction split</li> <li>temperature_split:<strong> </strong>Trian/test placement of that reaction/temperature combination for temperature split</li> <li>catalysishub_reactionID: Catalysis Hub reaction ID identifier for referencing catalysis hub database</li> <li>doi:<strong> </strong>digital object identifier of original publication for which DFT calculations were performed</li> </ul> <p> </p> <p> </p> <p><strong>Flux_flux_correlation_functions:</strong></p> <p>Directory containing flux-flux correlation function time series values for each reaction temperature combination. Values are organized in subdirectories, one for each of the 14 reaction. In each subdirectory .csv files are labeled by reaction number and temperature in Kelvin. Each csv file contains a column with time points [au of time] and the corresponding flux-flux correlation function value in units [1/(au of time)<sup>2</sup>].</p> <p> </p> <p><strong>Geometries:</strong></p> <p>Directory containing geometry files for each reaction. Geometries of reactants on the surface were shifted respect to those supplied by catalysis hub to create continuous reaction pathways where necessary. Geometry files are organized in subdirectories for each reaction. When complete nudged elastic band (NEB) minimum energy paths (MEP) were not available ,subdirectories contain a products.xyz, reactants.xyz, and TSstar.xyz file (reactions 1 to 11) otherwise the complete set of NEB MEP images labeled neb{n}.xyz is given (reactions 12, 13, 14).</p> <p> </p> <p> </p>
Supporting Information for the Journal Article "Quantum Chemical Data Generation as Fill-In for Reliability Enhancement of Machine-Learning Reaction and Retrosynthesis Planning"
<p>This data set contains all data produced when exploring the Williamson ether synthesis starting from iodoethane and phenol.</p> <p><br> The set is structures as follows:</p> <ul> <li>analysis: Contains the script used to analyze the exploration and the output of said script</li> <li>check_barrier: Contains the output of the manual calculations done to check the barrier of the reaction</li> <li>exploration: Contains the scripts used to initialize and carry out the exploration as well as the two starting structures as XYZ files</li> <li>raw_data: a dump of the MongoDB database with all the data produced during the exploration</li> </ul>
Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning
<p>With the development of advanced predictive modelling techniques, we are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic or biological characteristics based solely on DNA sequence information. These models, however, treat the DNA sequence as a mere collection of four, A, T, G and C, letters, thus dismissing the past physico-chemical advancements in science that can enable the use of more intricate information about nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming quantum mechanical methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A to C mutation rate constants.</p> <p>The DNA k-mer quantum mechanical parameters can also be found <a href="https://github.com/SahakyanLab/DNAkmerQM" target="_blank" rel="noopener">https://github.com/SahakyanLab/DNAkmerQM</a>, the corresponding research and development code from <a href="https://github.com/SahakyanLab/NucleicAcidsQM" target="_blank" rel="noopener">https://github.com/SahakyanLab/NucleicAcidsQM</a>, and the associated pre-print from <a href="https://doi.org/10.1101/2023.01.25.525597" target="_blank" rel="noopener">https://doi.org/10.1101/2023.01.25.525597</a>.</p>
Provably efficient machine learning for quantum many-body problems
<p>Raw data for the manuscript "Provably efficient machine learning for quantum many-body problems".</p>
Data for "Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations"
<p>This is the data set for the preprint <a href="https://arxiv.org/abs/2206.07605">arXiv:2206.07605</a> [cond-mat.mtrl-sci], obtained by the GPUMD code.</p> <p>Here are 6 directories.<br> 1). NEMD<br> 2). NEPpotential<br> 3). PDOS<br> 4). kappa-quenchRate<br> 5). kappa-size<br> 6). kappa-temperature<br> <br> 1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average.</p> <p>2). NEPpotential directory is the trained NEP potential.</p> <p>3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s.</p> <p>4). kappa-quenchRate directory contains HNEMD calculations of a-Si samples which are prepared using melt-quench temperature protocols with the quench rates covering from 10^{11} to 5x10^{12} K/s. In each case, 3 independent cycles are run.</p> <p>5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run.</p> <p>6). kappa-temperature directory contains HNEMD calculations of a-Si samples which are prepared for different targeted temperatures using slow quench rate of 10^{11} K/s.</p> <p> </p>
Data for "Machine learning of reduced quantum channels on NISQ devices"
<p>This dataset contains the data, figures and code of the publication <a href="https://doi.org/10.48550/arXiv.2405.12598">"Machine learning of reduced quantum channels on NISQ devices"</a>. I.e., LeNoM (Learning Noise Models) represents the core implementation of our approach.</p>
Supplemental data for "Large-scale quantum machine learning"
<p>This data supports "Large-scale quantum machine learning" by Tobias Haug, Chris N. Self, M. S. Kim (arxiv:2108.01039) https://arxiv.org/abs/2108.01039</p> <p>Related code can be found in the GitHub repository: (https://github.com/chris-n-self/large-scale-qml). The 'studies' folder here can be dropped inside the code repository in order to run the analysis scripts.</p> <p>Both 'processed' and 'unprocessed' data is provided. Unprocessed data is the qiskit measurement results for each case study, executed on the IBM Quantum device <em>ibmq_guadalupe</em> and <em>ibmq_toronto</em>. Processed is the Gram matrix evaluated from the measurements and the data vectors needed to fit support vector machine classifiers.</p>
Supporting data for "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics"
<p>Supporting data for "<a href="https://www.nature.com/articles/s41467-023-36666-y">Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics</a>" by M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen and V. Van Speybroeck, <em>Nature Communications</em>, <strong>2023</strong>, 14, 1008.</p> <p>This dataset contains examples of input files, submission and analysis scripts to train and use a machine learning potential based on the Schnet architecture for the proton hopping reaction in the H-CHA zeolite. The complete DFT training set, obtained by unbiasing the forces printed by CP2K (with PLUMED coupling), is stored as extended xyz files in the folders DFT/A-B/training_data.xyz where A=1-3 and A<B<5. More details on the folder architecture can be found in the README.md file.</p>
Data and code example for the article: "Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification"
<p>Data needed to reproduce the figures of <a href="https://arxiv.org/abs/2305.05881">https://arxiv.org/abs/2305.05881</a> and a simple code example of a quantum-convex-classical neural network used to train a sine versus cosine classification problem.</p>
Machine Learning Integrated High Quantum Yield Blue Light Carbon Dots for Real-time and On-site Detection of Cr(VI) in Groundwater and Drinking Water
<p>RGB和Kmeans提取后含有Cr(VI)水样的图像数据</p>
Data sets and machine learning models for: Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates
<p>The datasets and final machine learning model files for the manuscript "Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates". Citation should refer directly to the manuscript:</p> <ul> <li>Chung, Y.; Green, W. H. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. <em>Chemical Science </em><strong>2024,</strong> doi: <a href="https://doi.org/10.1039/D3SC05353A">10.1039/D3SC05353A</a></li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a>. </p> <p>Detailed information can be found in README.md file.</p> <p><br><strong>Details on the files</strong></p> <p>In the pretraining and finetuning set csv files, each column represents:</p> <ol> <li>rxn_smiles: atom-mapped reaction SMILES</li> <li>solvent_smiles: solvent SMILES</li> <li>ddGsolv: solvation free energy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>ddHsolv: solvation enthalpy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>dGsolv_reactant: solvation free energy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dGsolv_product: solvation free energy of product(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_reactant: solvation enthalpy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_product: solvation enthalpy of product(s) at 298K in kcal/mol (additional feature)</li> </ol> <p><strong>Data sets under 'RxnSolvKSE_dataset_v1.1.zip'</strong></p> <ul> <li>pretraining_set: contains the dataset used for pre-training <ul> <li>all_data: contains all calculated data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: contains both main prediction targets and additional feature for reaction-solvent pairs</li> <li>pretraining_solvent_info.csv: list of all solvents</li> <li>pretraining_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_500k_data: contains the chosen 500k data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_500k.csv: contains main prediction targets for reaction-solvent pairs</li> <li>pretraining_features_react_prod_dGsolv_dHsolv_500k.csv: contains additional features for reaction-solvent pairs</li> <li>train_test_split: contains the 5-fold random split training and test sets.</li> </ul> </li> </ul> </li> <li>finetuning_set: contains the dataset used for fine-tuning <ul> <li>all_data: contains all calculated data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: constains both main prediction targets and additional features for reaction-solvent pairs. The rxn_key column indicates whether the reaction is bimolecular hydrogen abstraction (bihabs), unimolecular hydrogen migration (intrahabs), or radical addition to a multiple bond (raddition). The 'fwd' and 'rev' each indicate forward and reverse reactions.</li> <li>finetuning_solvent_info.csv: list of all solvents</li> <li>finetuning_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_data: contains chosen data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_chosen.csv: contains main prediction targets for reaction-solvent pairs</li> <li>finetuning_features_react_prod_dGsolv_dHsolv_chosen.csv: contains additional features for reaction-solvent pairs</li> </ul> </li> </ul> </li> <li>experimental_set: contains the experimental rate constant data used to test the model. The original experimental data can be found at <a href="../record/7747557">https://zenodo.org/record/7747557</a>. <ul> <li> expt_rxn_atom_mapped_smiles.csv: contains the atom-mapped reaction SMILES used for the experimental data.</li> <li>expt_data_collected.xlsx: contains all experimental data and detailed information</li> <li>expt_rxn_solv_smiles_with_features_all.csv: contains the computed additional features for the experimental reaction-solvent pairs.</li> </ul> </li> </ul> <p><strong>Machine learning model files under 'RxnSolvKSE_ML_model_files.zip'</strong></p> <ul> <li>Contains the Chemprop machine learning model files for predicting ddGsolv and ddHsolv for a reaction-solvent pair. It takes atom-mapped reaction SMILES and solvent SMILES as inputs.</li> <li>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a></li> </ul>
Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning
<p>It contains reference training and test set configurations (and corresponding energy, forces, and virial stress values) for ZIF-8 and MOF-5.</p>
Data for figures of manuscript entitled: "On the Sample Complexity of Quantum Boltzmann Machine Learning"
<p>The zip file contains the data for each of the plots in the figures in the manuscript: "On the Sample Complexity of Quantum Boltzmann Machine Learning." The preprint version of this article can be found on arXiv: https://arxiv.org/abs/2306.14969</p>
Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning
<p>Dataset associated with the manuscript entitled "Unlocking the Predictive Power of Quantum-Inspired Representations for Intermolecular Properties in Machine Learning". </p> <p>See Readme file (markdown format) for details on how the data is structured in the "database" file.</p>
Data Repository for: Machine-learning the spectral function of a hole in a quantum antiferromagnet
<p>The machine-learning dataset of 51^3 ~1.3 × 10^5 density of states (DOS) of a mobile hole in the t-t'-t''-J model theoretically generated by using the self-consistent Born approximation in the three-dimensional parameter space of t′ ∈ [−0.5, 0.5], t′′ ∈ [−0.5, 0.5] and J ∈ [0.2, 1.0], with each parameter sampled on a 51-point uniform grid. The dataset is randomly partitioned into an 80/10/10 training (T), validation (V), and testing T split. Note that each DOS A(ω) was calculated on a 1201-point uniform grid of ω ∈ [−6t, 6t], then it was resampled on a 301-point uniform grid for the forward problem and on a 354-point uniform grid for the inverse problem. The dataset used in the inverse problem is limited to the parameter space of t′ ∈ [−0.5, 0], t′′ ∈ [0, 0.5] and J ∈ [0.2, 1.0]. To open the enclosed .npz files, use numpy.load() in python3.</p>
Data related to: MACHINE LEARNING AND QUANTUM MECHANICS APPROACH TO MORE CHEMICALLY-AWARE MOLECULAR DESCRIPTORS FOR MEDICINAL CHEMISTRY APPLICATIONS
<p>DEMIN VS QM ELECTRONIC POTENTIAL CORRELATIONS FOR THE N:= ATOM TYPE AND THE N1 ATOM TYPE</p> <p>dEmin versus H-bond basicity scale and H-bond acidity scale </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.