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37 results for “Quantum mechanics”
Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules
<p>Open challenges in computational drug design include the understanding and accurate description of solvent effects as well as collective dispersion interactions for realistic drug-like molecules. Both interactions profoundly influence the conformational stability of drug molecules and, consequently, the determination of other important quantum-mechanical (QM) observables. In this context, we here introduce the Aquamarine (AQM) dataset -- an extensive QM dataset that contains the structural and electronic information -- of 59,786 low-and high-energy conformers of 1,653 molecules containing up to 54 non-hydrogen atoms (including C, N, O, F, P, S and Cl). To gain insights into the solvent effects, we have carried out QM calculations of structures and properties in gas phase and in an aqueous solution modeled with implicit solvent. AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per molecular structure computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD supplemented with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By treating both molecule-solvent and dispersion interactions, the AQM dataset can help understand the impact of both interactions in structure-property and property-property relationships of realistic drug-like molecules. Therefore, we propose the AQM dataset as a benchmark for current state-of-the-art machine learning methods for property prediction as well as for the <em>de novo</em> generation of large and flexible (solvated) molecules with pharmaceutical and biological relevance.</p>
QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
<p>Here, we introduce QM7-X, a comprehensive dataset of > 40 physicochemical properties for ~4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures---comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-trans-and conformational isomers)---as well as 100 non-equilibrium structural variations thereof to reach a total of ~4.2 M molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly converged dataset of quantum-mechanically computed physical and chemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing <em>in silico</em> design of molecules with targeted properties.</p> <p>The dataset is provided in eight HDF5 based files (compressed in .XZ files). One can also find here a README file with technical usage details and examples of how to access the information stored in the dataset (see createDB.py). </p> <p>*The paper explaining the generation of data stored in QM7-X can be found in <em>Sci Data</em> 8, 43 (2021). DOI: 10.1038/s41597-021-00812-2 . arXiv: https://arxiv.org/abs/2006.15139 .</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>
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>
Data on quantum mechanics and electro-magnetic excitation of particles involved in combustion process
<p><span>Distribution of average free electron energy in the body and the closest vicinity of the high frequency spark, arc and corona gaseous discharge</span></p>
Dataset for "Engineering defect clustering in diamond-based materials for technological applications via quantum mechanical descriptors"
<p>The unique set of extreme physical properties makes diamond an ideal candidate for applications in the energy industry such as in high-power and high-frequency electronics as well as in electrochemistry and photovoltaics. Furthermore, dopant-vacancy complexes in diamond can be exploited for further development of quantum computers, single-photon emitters, high-precision magnetic field sensing and nanophotonic devices. While certain dopant-vacancy complexes are well-studied, studies of other dopant/vacancy clusters are focused mostly on defect detection while investigations on how to tune their electronic and optical properties for specific applications is mostly omitted. To this aim, we attempted to reveal coupled structural-electronic features and their effect on the band gap of such defects through first principle calculations. We investigated four different defect types: a) dopant-vacancy complexes (X-V), b) two dopants as nearest neighbours (X-X), c) two dopants separated by one carbon atom (X-C-X) and d) two dopants separated by a vacancy (X-V-X). For each of these configurations, we considered Al, B, N, P and Si as dopant atoms. This dataset contains input files needed to reproduce every ground state geometry used in our study.</p>
Source Data for the paper: "Quantum-classical simulations reveal the photoisomerization mechanism of a prototypical first-generation molecular motor"
<p>This dataset contains the raw data for the results shown in the paper.</p> <p>For each figure of the paper (main text), one directory with data file(s) is provided.</p>
Data and codes for "A squeezed mechanical oscillator with milli-second quantum decoherence"
<p>Here you find the raw data files and processing Python scripts for plots presented in "A squeezed mechanical oscillator with milli-second quantum decoherence" Amir Youssefi, et.al. Nature Physics 2023</p>
Molecular geometries and energies from quantum mechanical calculations and small molecule force field evaluations.
<p>Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction.<br> The quality of these studies relies on the quality of the force fields used to represent the systems. <br> Focusing on small molecules of fewer than 50 heavy atoms, this data compares nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2.<br> On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data.<br> <br> The data was created using scripts of the <a href="https://github.com/MobleyLab/benchmarkff/commit/fa45247aa9867f504c02eb6f62d8459a94a0a936">benchmarkff github repository</a>.</p> <p>A corresponding manuscript is submitted, a preprint is available on ChemRxiv:<br> <a href="https://doi.org/10.26434/chemrxiv.12551867.v2 ">Lim, Victoria T.; Hahn, David F.; Tresadern, Gary; Bayly, Christopher I.; Mobley, David (2020): Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields. ChemRxiv. Preprint</a></p> <p>Read below or the file README.md for further information and description of the content:</p> <pre><code class="language-markdown"># README Version: 04 Nov 2020 For Python scripts that are NOT found in these directories, please check the [BenchmarkFF Github repo](https://github.com/MobleyLab/benchmarkff/tree/master/tools). ## Procedure 1. Prep OPLS3e file for analysis: standardize format by OpenEye in case of differences and convert from kJ/mol to kcal/mol. ``` cd prep python convert_extension.py -i opls3e_minimized.sd -o opls3e.sdf ``` 2. Remove mols that couldn't parameterize by ALL FFs. ``` python get_by_tag.py -i opls3e.sdf -s "SMILES QCArchive" -list trim3.txt -o trim3_full_opls3e.sdf ``` 3. Run analysis. ``` conda activate parsley # calc ddE, RMSD, and TFD distributions python compare_ffs.py -i match.in -t 'SMILES QCArchive' --plot > metrics.out # match_minima, only in 01_analysis_all and 02_analysis_all_smaller_cutoff python match_minima.py -i match.in --plot --cutoff 1.0 --readpickle # look at specific subsets, only in 01_analysis_all python color_by_moiety.py -i match.in -p metrics.pickle -s N-N.dat azetidine.dat octahydrotetracene.dat -o scatter_tfd_3_ # look at outliers,only in 01_analysis_all and 02_analysis_all_smaller_cutoff python tailed_parameters.py -i refdata_trim_overlap_full_openff_unconstrained-1.2.0.sdf -f <offxml file> --metric 'TFD' --cutoff 0.12 --tag "TFD to trim_overlap_full_qcarchive.sdf" --tag_smiles "SMILES QCArchive" > output_tfd.dat ``` ## Brief description of contents * High level: ``` . ├── 00_prep │ ├── convert_extension.py │ ├── opls3e_minimized.sd OPLS3e minimized structures from Schrodinger Maestro │ ├── opls3e.sdf standardized through OpenEye tools │ ├── opt_openff*.sdf OpenFF minimized conformations ├── 01_analysis_all compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) ├── 02_analysis_all_smaller_cutoff compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) with a smaller cutoff of .3 for match_minima ├── 03_analysis_latest_ffs compare only the latest versions of ffs (qm, GAFF2, MMFF94S, OpenFF-1.2, OPLS3e) ├── 04_analysis_openff_only compare only OpenFF ffs (qm, Smirnoff, OpenFF-X.X) └── README.md ``` * Inside an output directory: ``` YY_analysis_* various output files of above mentioned scripts, some are listed and described below: ├── bar*.png parameter coverage bar plots ├── ddE.dat relative energies data ├── fig_density_*.png scatter plots of ddE vs (RMSD or TFD) for each force field ├── match.in input file for compare_ffs.py ├── metrics.out output file for compare_ffs.py ├── metrics.pickle pickle file for compare_ffs.py -- you can read this into compare_ffs instead of rerunning the full analysis ├── refdata_*.sdf output SDF files with stored RMSD / TFD scores with reference to QM for each structure ├── relene_*.dat relative energies of matched conformers ├── ridge_dde.png compared energies plot ├── ridge_rmsd.svg compared rmsds plot ├── ridge_tfd.svg compared tfds plot ├── fig_scatter_*.png scatter plots of ddE vs (RMSD or TFD). these are noisy; I don't use these ├── trim3_*.sdf input SDF files for compare_ffs.py listed in match.in file ├── violin*.* violin plot showing ddE distributions ``` </code></pre>
Development of predictive models of the kinetics of a hydrogen abstraction reaction combining quantum-mechanical calculations and experimental data
<p>The files contain the electronic structure calculations for all the levels of theory tested in this work.</p>
Data and code for figures in "A dissipative quantum reservoir for microwave light using a mechanical oscillator"
<p>Data and code used to produce the figures in "A dissipative quantum reservoir for microwave light using a mechanical oscillator".</p> <p>The code is tested with Python 2.7.10, Matplotlib 2.0.0b4, Scipy 0.18.0.</p>
Alice in Quantum Land — Quantum Mechanics and its influence in (pop) culture
<p>In this seminar we will discuss Quantum Mechanics (QM) not so much from the traditional point of view of Physics, but to assess the inevitable “reverberations” that this discipline has had, and still has, in popular culture. Indeed, one of the aspects known even to the layman is the strangeness of the laws that govern the subatomic world, where totally counterintuitive things happen. Those who are used to doing science in the traditional way, resting their research on solid epistemological foundations, when confronted with QM must “suspend judgment” and very often use it – very pragmatically – as a simple tool (which Physicists have shown they can do very well).</p> <p>The seminar will focus on the bewilderment that the forerunners of this discipline already had to overcome to accept the “rules” it imposed, and we will see that among them even eminent minds made an enormous effort to accept these rules, when not even going so far as to reject this “new Physics” altogether.</p> <p>The seminar will then briefly explore the more “pop” side, which is how, from the point of view of popularizing science, QM offers excellent tools to intrigue a lay audience.</p>
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>
Binding Energies of Interstellar Relevant S-bearing Species on Water Ice Mantles: A Quantum Mechanical Investigation
<p>This Supporting Material contains:</p> <ul> <li>Fractional coordinates of DFT optimized adsorption complexes for crystalline periodic ice models in <a href="https://www.moldraw.unito.it/_sgg/m1m1s43_1.htm">.mol</a> format, editable with <a href="http://www.moldraw.unito.it/">MOLDRAW</a>, using <a href="http://www.crystal.unito.it/">CRYSTAL17</a> computer code;</li> <li>Fractional coordinates of HF-3c optimized adsorption complexes for amorphous periodic ice models in <a href="https://www.moldraw.unito.it/_sgg/m1m1s43_1.htm">.mol</a> format, editable with <a href="http://www.moldraw.unito.it/">MOLDRAW</a>, using <a href="http://www.crystal.unito.it/">CRYSTAL17</a> computer code;</li> <li>Images of the adsorption features at crystalline periodic ice models, in which electrostatic potential maps, spin density maps (when available) and vibrational features are displayed;</li> <li>A pdf file with a thorough guide to the computation of BEs and the basis sets employed for the calculations.</li> </ul> <p> </p>
Datasets and geometries for "MORE-Q, Dataset for molecular olfactorial receptor engineering by quantum mechanics"
<p>We introduce the MORE-Q dataset, a quantum-mechanical (QM) dataset encompassing the structural and electronic data of non-covalent molecular sensors formed by combining 18 mucin-derived olfactorial receptors with 102 body odor volatilome (BOV) molecules. To have a better understanding of their intra- and inter-molecular interactions, we have performed accurate QM calculations in different stages of the sensor design and, accordingly, MORE-Q splits into three subsets: i) MORE-Q-G1: QM data of 18 receptors and 102 BOV molecules, ii) MORE-Q-G2: QM data of 23, 838 BOV-receptor configurations, and iii) MORE-Q-G3: QM data of 1, 836 BOV-receptor-graphene systems. Each subset involves geometries optimized using GFN2-xTB with D4 dispersion correction and up to 39 physicochemical properties, including global and local properties as well as binding features, all computed at the tightly converged PBE+D3 level of theory. By addressing BOV-receptor-graphene systems from a QM perspective, MORE-Q can serve as a benchmark dataset for state-of-the-art machine learning methods developed to predict binding features. This, in turn, can provide valuable insights for developing the next-generation mucin-derived olfactory receptor sensing devices.</p> <p>The dataset is provided in 3 HDF5 based files. One can also find here a README file with technical usage details and examples of how to access the information stored in the dataset (see createDF.py). We also offer a Github repository for user guide, see https://github.com/LiC1117/MORE-Q.</p> <p>For more details, one can refer to the manuscript doi: <a href="https://doi.org/10.1038/s41597-025-04616-6" rel="nofollow">https://doi.org/10.1038/s41597-025-04616-6</a></p>
Data and codes for the article "Quantum collective motion of macroscopic mechanical oscillators"
Open the record for dataset details and reuse information.
Quantum mechanical double slit for molecular scattering
<p>Interference observed in a double-slit experiment most conclusively demonstrates the wave properties of particles. We construct a quantum mechanical double-slit interferometer by rovibrationally exciting D2 (v=2, j=2) molecules in a biaxial state using Stark-induced adiabatic Raman passage. In D2(v=2, j=2)→D2(v=2, j'=0) rotational relaxation via a cold collision with ground state He, the entangled bond axis orientations in the biaxial state act as two slits generating two indistinguishable quantum mechanical pathways connecting initial and final states of the colliding system. The interference disappears when we decouple the two orientations of the bond axis by separately constructing the uniaxial states of D2, unequivocally establishing the double-slit action of the biaxial state. This double slit opens new possibilities in the coherent control of molecular collisions.</p>
Evaluation of the pKa's of Quinazoline Derivatives : Usage of Quantum Mechanical Based Descriptors
<p>In this study, several quantum mechanical-based computational approaches have been used in order to propose accurate protocols for predicting the p<em>K<sub>a</sub></em>’s of quinazoline derivatives, which constitute a very important class of natural and synthetic compounds in organic, pharmaceutical, agricultural and medicinal chemistry areas. Linear relationships between the experimental p<em>K<sub>a</sub></em>’s and nine different DFT descriptors (atomic charge on nitrogen atoms (<em>Q</em>(N), ionization energy (<em>I</em>), electron affinity (<em>A</em>), chemical potential (m), hardness (h), electrophilicity index (w), fukui functions (<em>f <sup>+</sup></em>, <em>f <sup>-</sup></em>), condensed dual descriptor (D<em>f</em>) and local hypersoftness (s<sup>(2)</sup>)) were considered. Several DFT methods (a combination of five DFT functionals and two basis sets) in conjunction with two different implicit solvent models were tested, and among them, M06L/6-311++G(d,p) level of theory employing the CPCM solvation model was found to give the strongest correlations between the DFT descriptors and the experimental p<em>K<sub>a</sub></em>’s of the quinazoline derivatives. The calculated atomic charge on N<sub>1</sub> atom (<em>Q</em>(N<sub>1</sub>)) was shown to be the best descriptor to reproduce the experimental p<em>K<sub>a</sub></em>’s (R<sup>2</sup>=0.927), whereas strong correlations were also derived for <em>A</em>, w, m, and Δ<em>f</em>. In the last part, the applicability of isodesmic reaction scheme to the p<em>K<sub>a</sub></em> prediction of quinazoline derivatives was tested, and the calculated <em>A</em> was shown to be a well-established method for the classification of molecules, and thus, for the identification of a suitable reference molecule for the calculations. The QM-based protocols presented in this study will enable fast and accurate high-throughput p<em>K<sub>a</sub></em> predictions of quinazoline derivatives and the relationships derived can be effectively used in data generation for successful machine learning models for p<em>K<sub>a</sub></em> predictions.</p>
Roger Penrose Why Quantum Mechanics Is an Inconsistent Theory
<p>Roger Penrose Why Quantum Mechanics Is an Inconsistent Theory</p>
Data from: Active-feedback quantum control of an integrated, low-frequency mechanical resonator
<p>Source data for Figures.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.