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15 results for “Quantum chemistry”
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>
Conformation and structural features of diuron and irgarol: insights from quantum chemistry calculations.
<p>A set of conformational relevant structures of Diuron and Irgarol (two biocides) obtained from conformational analyses carried out using Density Functional Theory (DFT). The geometries are given in the mol2 format.</p>
Quantum calculation results for "Butyl Acetate Pyrolysis and Combustion Chemistry: Mechanism Generation and Shock Tube Experiments"
<p>This repository contains the quantum calculation results associated with the paper "Butyl Acetate Pyrolysis and Combustion Chemistry: Mechanism Generation and Shock Tube Experiments" by Xiaorui Dong, Gianmaria Pio, Farhan Arafin, Andrew Laich, Jessica Baker, Erik Ninnemann, Subith S. Vasu, and William H. Green.</p> <p>In the BA_QM.zip, there are five subfolders:</p> <ul> <li>The "BA_Habs" folder has 18 entries related to the calculations of butyl acetate H abstraction reactions</li> <li>The "BA_Retroene" folder has 6 entries related to the calculations of butyl acetate retro-ene reactions</li> <li>The "Species" folder has 606 entries related to the calculations of non-TS species included in the kinetic mechanisms.</li> <li>The "TS_XYZ" and "XYZ" folders contain the XYZ files of the calculated TS and non-TS geometries, respectively.</li> </ul> <p>For each species and TS calculation, the CBS-QB3 optimization/single point energy calculation is stored in the "composite" folder, the frequency calculation is stored in the 'freq' folder, and scan jobs for torsional modes (if available) are stored in the 'scan_XXXX' folders. For reaction and TS entries, folders are named in a user-readable way. For species, folders are named according to their SMILES representation.</p>
QM and COSMO-RS calculation results and experimental data for: Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods
<p>This dataset contains the calculation results and the experimental data compiled from literature for the manuscript "Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods". Citations should refer directly to the manuscript (Chung, Y.; Green, W. H. Computing kinetic solvent effects and liquid phase rate constants using quantum chemistry and COSMO-RS methods. <em>J. Phys. Chem. A</em> <strong>2023</strong>, 127, 27, 5637–5651. doi: <a href="https://doi.org/10.1021/acs.jpca.3c01825">10.1021/acs.jpca.3c01825</a>).This includes:</p> <ul> <li>expt_data_collected.xlsx: Experimental rate constants of various liquid phase reactions collected from various sources</li> <li>For each levels of theory used for gas-phase quantum chemical calculations and COSMO-RS calculations: <ul> <li>Gas-phase quantum chemical calculation results (output log files) and computed gas phase rate constants</li> <li>COSMO-RS calculation results and computed solvation free energies</li> <li>Predicted liquid phase rate constants and relative rate constants </li> </ul> </li> </ul> <p> </p>
Reducing the runtime of fault-tolerant quantum simulations in chemistry through symmetry-compressed double factorization
<p>Data repository for "Reducing the runtime of fault-tolerant quantum simulations in chemistry through symmetry-compressed double factorization" <a href="https://arxiv.org/abs/2403.03502" target="_blank" rel="noopener">arXiv:2403.03502</a>.</p>
Data for Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization
<p>Dataset substantiating the claims in <a href="https://arxiv.org/abs/2212.07957">[2212.07957] Accelerating Quantum Computations of Chemistry Through Regularized Compressed Double Factorization (arxiv.org)</a></p>
Replication Data for: Exact two-component Hamiltonians for relativistic quantum chemistry: Two-electron picture-change corrections made simple
<p>Based on self-consistent field (SCF) atomic mean-field (amf) quantities, we present two simple, yet computationally efficient as well as numerically accurate algebraic approaches to remedy both scalar-relativistic <em>and</em> spin-orbit two-electron picture-change effects (PCE) arising within an exact two-component (X2C) Hamiltonian framework. Both approaches, dubbed as amfX2C and e(xtended)amfX2C, allow us to uniquely tailor PCE corrections to either mean-field models, <em>viz</em>. Hartree–Fock or Kohn–Sham DFT, in the latter case also avoiding the need of a point-wise calculation of exchange–correlation PCE corrections. We assess the numerical performance of these PCE correction models on spinor energies of group-18 (closed-shell) and group-16 (open-shell) diatomic molecules, achieving a consistent ≈ 10−5-Hartree accuracy with regard to reference four-component data. Additional tests include SCF calculations of molecular properties such as absolute contact density and contact density shifts in copernicium fluoride compounds (CnFn, n=2,4,6), as well as equation-of-motion coupled cluster calculations of X-ray core ionization energies of 5<em>d</em> and 6<em>d</em>-containing molecules where we observe an excellent agreement with reference data. To conclude, we are confident that our (e)amfX2C PCE correction models constitute a fundamental milestone towards a universal and reliable relativistic two-component quantum chemical approach, maintaining the accuracy of the parent four-component one at a fraction of its computational cost.</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>
Integrals for "Quantum Simulations of Chemistry in First Quantization with any Basis Set"
<p>Integrals used to study scalings and carry out resource estimations. We did not upload the matrices for basis sets with 32000 functions because of large matrix sizes (8 GB per matrix). These integrals correspond to matrix elements of the Hamiltonian in the original basis (Eq. (II.1) and Eq.(III.6)) in this version of the manuscript: (<a href="https://arxiv.org/abs/2408.03145v2">arXiv:2408.03145v2</a> [quant-ph]), and not in the Pauli string representation. </p> <p>UPDATE: in the previous version of matrices in dual plane wave basis, kinetic energy + electron nuclear term gave the correct one-body contribution, but each individual file had an error in it. In this version, we updated the correct kinetic energy and electron nuclear matrices. Notice that electron-nuclear files are just diagonal elements so you need to convert it to a matrix to get correct kinetic + electron nuclear term. Also, dual plane waves electron-electron matrix includes a factor of 1/2 in it. FCIDUMP files are the same as in previous version.</p>
Data for "Long-range electron-electron interactions in quantum dot systems and applications in quantum chemistry"
<p>This dataset contains the data files and plotting scripts for figures of "Long-range electron-electron interactions in quantum dot systems and applications in quantum chemistry".</p> <p>Questions can be addressed to: johannes.knoerzer@eth-its.ethz.ch and c.j.vandiepen@tudelft.nl</p>
Quantum chemistry reference data set for random hydrogen clusters
<p>This data set contains high-level quantum chemistry data (CCSD(T)/def2-QZVPP) for a set of 120,000 randomly-generated hydrogen clusters, along with data from other levels of theory (HF, MP2, CCSD), including 3 density functionals (PBE, B3LYP, omegaB97M-V) and 4 semiempirical models (AM1, PM7, GFN1, & GFN2). This entry also includes the workflow scripts used to generate the data and the post-processing scripts used to analyze and visualize it.</p> <p>By the standards of quantum chemistry data sets, this is a large and challenging test for electronic structure methods and models. These structures tend to have open-shell ground states that can be difficult to find.</p>
Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry
<p>Q-Chem output files, extracted SMILES, activation energies, and enthalpies of formation for 16,452 B97-D3/def2-mSVP reactions and for 12,001 ωB97X-D3/def2-TZVP reactions. The raw log files are stored in <em>b97d3.tar.gz</em> and <em>wb97xd3.tar.gz</em> for B97-D3/def2-mSVP and ωB97X-D3/def2-TZVP data, respectively. Each archive contains a separate folder for each reaction. Within each folder are three log files for a reaction corresponding to reactant, product, and transition state. Each log file contains the output of a geometry optimization and harmonic vibrational analysis.</p> <p>Atom-mapped SMILES, activation energies, and enthalpies of formation for each reaction are listed in the comma-separated values files <em>b97d3.csv</em> and <em>wb97xd3.csv</em>. The reactions are listed in the same order as the corresponding folders in the archive files.</p> <p>Additional archives containing log files for all successfully optimized transition states are stored in <em>ts_with_dup_b97d3.tar.gz</em> and <em>ts_with_dup_wb97xd3.tar.gz</em>. There are 69,366 B97-D3/def2-mSVP transition states and 24,987 ωB97X-D3/def2-TZVP transition states. These data are better used with caution because they contain many duplicate transition states and the corresponding reactants and products are not known.</p>
Mirrorplots for "Quantum chemistry based prediction of electron ionization mass spectra for environmental chemicals"
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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>
Ames Quantum Chemistry
Ames Quantum Chemistry Dataset collects electronic structure, reaction kinetics, and dynamics data calculated at Ames Research Center. This includes potential energy curves and surfaces as well as the reaction cross sections and rate coefficients.
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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.