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220 results for “force field”
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field, simulation files and 100 ns trajectory for GROMACS simulation engine v5
<p>All runs were performed with GROMACS simulation engine v5 and CHARMM36 additive force field parameters obtained from MacKerell lab website (http://mackerell.umaryland.edu/charmm_ff.shtml, also available at</p> <p>https://doi.org/10.5281/zenodo.209080). Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 100ns trajectory (preceded with equilibration). </p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p>
Methanol-water mixtures obtained with the OPLS force field and SPCE water model
<p>These are Gromacs trajectories, 20 ps each, with MD information corresponding to pure water (one trajectory), methanol (one trajectory), and 2:1 molar mixtures of the two (10 trajectories). The pure water system contains 800 molecules, pure methanol contains 400 molecules, and each of the mixtures contains 400 water molecules and 200 methanol molecules. The force field employed is OPLS and water was simulated with the rigid SPCE model.</p> <p>These trajectories are made available with the main purpose of being used to run one of the DoSPT tutorials:</p> <p>http://dospt.org/index.php/Tutorial_2:_entropy_of_mixing_of_methanol%2Bwater</p>
MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M CsCl, Berger force field for lipids, Dang's for Cs+ and ffgmx for Cl-
<p>MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M CsCl (102 POPC, 26 POPS, 4290 WAT, 106 Cs+, 80 Cl-). Additional Cs+ cations added to neutralize the negative charge of POPS. Berger force field for lipids, Dang's for Cs+ (), ffgmx for Cl- are employed. Gromacs 4.0.7, T=310K, 200 ns trajectories were calculated with the last 50 ns stored here).<br> K+ nonbonding parameters (from Dang's Cs+ from JPC B 1999, 103, 8195):<br> sig=0.383086, eps=0.41840</p> <p>Used in:</p> <p>P. Jurkiewicz, L. Cwiklik, A. Vojtiskova, P. Jungwirth, M. Hof, Structure, Dynamics, and Hydration of POPC/POPS Bilayers Suspended in NaCl, KCl, and CsCl <br> <em>BBA Biomembranes </em>2012<em>, 1818, 609-616.</em><br> DOI: 10.1016/j.bbamem.2011.11.033</p>
MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M KCl, Berger force field for lipids, Dang's for K+ and ffgmx for Cl-
<p>MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M KCl (102 POPC, 26 POPS, 4290 WAT, 106 K+, 80 Cl-). Additional K+ cations added to neutralize the negative charge of POPS. Berger force field for lipids, Dang's for K+ (), ffgmx for Cl- are employed. Gromacs 4.0.7, T=310K, 200 ns trajectories were calculated with the last 50 ns stored here).K+ nonbonding parameters (from Dang's JPC B 1999, 103, 8195 based on Vacha et al. Biophys. J 2009, 96, 4493.):<br> sig=0.3048655 eps=0.418400</p> <p>Used in:</p> <p>P. Jurkiewicz, L. Cwiklik, A. Vojtiskova, P. Jungwirth, M. Hof, Structure, Dynamics, and Hydration of POPC/POPS Bilayers Suspended in NaCl, KCl, and CsCl <br> <em>BBA Biomembranes </em>2012<em>, 1818, 609-616.</em><br> DOI: 10.1016/j.bbamem.2011.11.033</p>
MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M NaCl, Berger force field for lipids and ffgmx for ions
<p>MD simulation trajectory of a POPC/POPS (4:1) bilayer with 1M NaCl (102 POPC, 26 POPS, 4290 WAT, 106 Na+, 80 Cl-). Additional Na+ cations added to neutralize the negative charge of POPS. Berger force field for lipids and ffgmx for ions are employed. Gromacs 4.0.7, T=310K, 200 ns trajectories were calculated with the last 50 ns stored here.</p> <p>Used in:</p> <p>P. Jurkiewicz, L. Cwiklik, A. Vojtiskova, P. Jungwirth, M. Hof, Structure, Dynamics, and Hydration of POPC/POPS Bilayers Suspended in NaCl, KCl, and CsCl <br> <em>BBA Biomembranes </em>2012<em>, 1818, 609-616.</em><br> DOI: 10.1016/j.bbamem.2011.11.033</p> <p> </p>
Vector Force Fields (VFF) Video Results
<p>Video results for PhD Thesis of Vector Force Fields (VFF) for collision avoidance and navigation in vineard simulation.<br><br><span><a href="https://zenodo.org/uploads/14116726" target="_blank" rel="noopener noreferrer">VFFLargeGap.mp4</a></span><span>: VFF algorithm for a relative bigger gap then the following results.</span></p> <div><a href="https://zenodo.org/api/records/14116726/draft/files/VFFMediumGap.mp4/content" target="_blank" rel="noopener noreferrer">VFFMediumGap.mp4</a>: <span>VFF algorithm for a medium gap then the following results.</span></div> <div> <div> </div> <div><a href="https://zenodo.org/api/records/14116726/draft/files/VFFMediumGap.mp4/content" target="_blank" rel="noopener noreferrer">VFFMediumGap.mp4</a>: <span>VFF algorithm for a relative smaller gap then the following results.</span></div> </div>
Datasets and scripts for the publication "Insights into Defect Cluster Formation in Non-Stoichiometric Wustite (Fe1-xO) at Elevated Temperatures: Accurate force field from Deep Learning"
<div> <div> <div> <div> <p><strong>All the datasets and scripts for the publication"Insights into Defect Cluster Formation in Non-Stoichiometric Wustite (Fe<sub>1-x</sub>O) at Elevated Temperatures: Accurate force field from Deep Learning".</strong></p> <p>This database contains high-fidelity datasets for non-stoichiometric wüstite (Fe₁₋ₓO), including atomic coordinates, energies, and forces generated through ab initio molecular dynamics (AIMD) and refined using Deep Potential (DP) training. The dataset encompasses bulk phases, vacancy structures, and surface orientations, enabling accurate modeling of defect clusters and thermodynamic properties. It supports machine-learning force field development, offering insights into defect formation and large-scale simulations of Fe₁₋ₓO systems at elevated temperatures.</p> </div> </div> </div> </div> <div> <p>Description of the File Structure of Fe1-xO_DeepMD_Code_Datasets_Analysis.zip:</p> <p>1. `<code>init</code>` Folder <br>This folder contains the foundational datasets and inputs used for training and developing the machine-learning force field for Fe₁₋ₓO. </p> <blockquote> <p>1.1 `<code>01.train_data</code>` Subfolder <br>This folder organizes data related to the initial training of the Deep Potential (DP) model. <br>- `<code>dpmd_dataset</code>`: Processed dataset ready for DeepMD training, containing atomic configurations, forces, and energies.<br>- `<code>dpmd_rawfiles</code>`: Raw files from ab initio molecular dynamics (AIMD) simulations, serving as the source for generating training datasets.</p> <p>1.2 `<code>02.develop_data</code>` Subfolder<br>Contains `<code>.vasp</code>` files representing structural data used to develop and refine the force field. The structures include bulk, vacancy, and surface configurations of Fe₁₋ₓO. <br>- Files labeled `<code>bulk</code>` represent bulk Fe₁₋ₓO systems with varying lattice constants. <br>- Files labeled `<code>defect</code>` represent Fe and O vacancy structures (single and double vacancies). <br>- Files labeled `<code>surface</code>` represent Fe₁₋ₓO surface structures in various crystallographic orientations. <br><br></p> </blockquote> <p>2. `<code>run</code>` Folder<br>This folder contains files and logs generated during iterative training and testing of the DP force field, as well as subfolders for each iteration of the training process. </p> <blockquote> <p>2.1 Iteration Folders (`<code>iter.000000</code>` to `<code>iter.000024</code>`):<br>Each folder represents an iteration in the iterative refinement of the DP model, with three subfolders: <br>- `<code>00.train</code>`: Contains training data and outputs for the DP model during the current iteration. <br>- `<code>01.model_devi</code>`: Tracks deviations between DP predictions and ab initio results, guiding dataset selection for the next iteration. <br>- `<code>02.fp</code>`: Stores first-principles (FP) results from CP2K used to improve DP model accuracy. </p> <p>2.2 Other Key Files: <br>- `<code>cp2k.input</code>`: Input file for CP2K, used for performing ab initio calculations on configurations during the iterative process. <br>- `<code>dpdispatcher.log</code>`: Log file tracking the progress of data dispatching and task execution. <br>- `<code>dpgen.log</code>`: Log file recording operations of DPGEN during dataset generation and force field development. <br>- `<code>dpgen_nohup.sh</code>`: Script for running DPGEN in the background. <br>- `<code>machine_slurm_cp2k.json</code>`: Configuration file specifying computing resources for CP2K simulations in a cluster environment. <br>- `<code>param_cp2k.json</code>`: Parameter file for CP2K calculations, defining simulation settings. <br>- `<code>record.dpgen</code>`: Record of iterative processes, including input parameters and outputs for each stage.</p> </blockquote> <p>This organized structure ensures a systematic approach to dataset preparation, model training, and iterative refinement for developing accurate machine-learning potentials for Fe₁₋ₓO.</p> <p>graph-compress.0330.pb is the final compressed DeepMD potential parameters.</p> </div>
Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.
<p>ML_AB_H: Datasets of 64 water molecules and a single proton with 64 water molecules.</p> <p>ML_AB_VP2: Datasets of a single V^2+ ion with 64 water molecules.</p> <p>ML_AB_VP3: Datasets of a single V^3+ ion with 64 water molecules.</p> <p>ML_AB_FeP2: Datasets of a single Fe^2+ ion with 64 water molecules.</p> <p>ML_AB_FeP3: Datasets of a single Fe^3+ ion with 64 water molecules.</p> <p>ML_AB_CuP1: Datasets of a single Cu^+ ion with 64 water molecules.</p> <p>ML_AB_CuP2: Datasets of a single Cu^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP2: Datasets of a single Ru^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP3: Datasets of a single Ru^3+ ion with 64 water molecules.</p> <p>ML_AB_AgP1: Datasets of a single Ag^+ ion with 64 water molecules.</p> <p>ML_AB_AgP2: Datasets of a single Ag^2+ ion with 64 water molecules.</p> <p>ML_AB_O2: Datasets of a single O2 ion with 64 water molecules.</p> <p>ML_AB_O2N1: Datasets of a single O2^- ion with 64 water molecules.</p> <p>ML_AB_water: Datasets of 64 water molecules presenting bulk water and 64 water molecules representing water slab.</p> <p>All datasets were generated by VASP using PAW, plane wave basis sets with cutoff energy of 520 eV and RPBE+D3 exchange-correlation functional with zero-damping. All ab initio calculations were done on extended systems with periodic boundary conditions. See details in <a href="https://doi.org/10.48550/arXiv.2409.11000">https://doi.org/10.48550/arXiv.2409.11000</a>.</p>
Amyloid-beta 16-22 peptide monomer simulation with the CHARMM-Drude force field and OpenMM (Run 1)
<p>Amyloid-beta 16-22 peptide (monomer) simulations with the CHARMM-Drude force field and OpenMM. This is the first independent simulation runs out of 3.</p> <p>Part 1-2 are 200 ns long, 3-8 are 100 ns each. Total trajectory length is 1 microseconds. Frame saving frequency is 10 ps.</p> <p>The system contains ~ 150 mM NaCl.</p>
Amyloid-beta 16-22 peptide monomer simulation (150 mM NaCl) with the CHARMM36m force field and Gromacs (Run 3)
<p>MD simulations of the Amyloid-beta 16-22 monomer at 150 mM NaCl concentration with CHARMM36m force field and Gromacs. This repository contains the third out of three independent runs. </p> <p>Files belong to the publication "<a href="https://doi.org/10.1021/acs.jcim.0c01063">https://doi.org/10.1021/acs.jcim.0c01063</a>"</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with Gromacs 2018.3</p> <p>Total simulation time is 500 ns. Frames are saved with 100 ps frequency. </p>
Different functional networks underlying human walking with pulling force fields acting in forward or backward directions
<p><span>Walking with pulling force fields acting at the body center of mass (in </span><span>the </span><span>forward or backward directions) is compatible with inclined walking and is used in clinical practice for gait training. From the perspective of known differences in the motor strategies that underlie walking with the respective force fields, the present study elucidated whether the adaptation acquired by walking on a split-belt treadmill with either one of the force fields affects subsequent walking in other directions. Walking with the force field induced an adaptive and de-adaptive behavior of the subjects</span><span>, with the aspect evident </span><span>in the anterior breaking and posterior propulsive impulses of the ground reaction force as parameters. In the parameters, </span><span>the </span><span>adaptation acquired during walking with </span><span>a force field </span><span>acting in one direction was transferred to that in </span><span>the opposite direction only partially. </span><span>Furthermore, </span><span>the adaptation that occurred </span><span>while walking in </span><span>a force field </span><span>in one direction was rarely washed out by subsequent walking in </span><span>a force field </span><span>in </span><span>the opposite direction</span> <span>and thus was maintained independently of the other</span><span>. These results demonstrated possible independence in the neural functional networks capable of controlling walking in each movement task with </span><span>an opposing force field</span><span>.</span></p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 2)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the second out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 3)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the third out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Amyloid-beta 16-22 peptide dimer simulation (without salt) with the CHARMM-Drude force field and OpenMM (Run 1)
<p>MD simulations of the Amyloid-beta 16-22 dimer at 0 mM NaCl concentration with CHARMM-Drude force field and OpenMM. Initial structure is obtained from CHARMM-GUI. In the initial configuration, two amyloid-beta 16-22 monomers are not interacting. This repository contains the first out of three independent runs.</p> <p>All the simulation parameters and force field files are uploaded into this repository. Simulations are done with OpenMM v. 7.5.1.</p>
Effects of Reanalysis Forcing Fields on Ozone Trends and Age of Air from a Chemical Transport Model
<p>This dataset is based on the global off-line 3-D chenmical transport model (TOMCAT/SLIMCAT) forced with ECMWF reanalyses (ERA-Interim and ERA5) to compare the performance of the stratospheric ozone simulations. Each field is separately saved as NETCDF file. Each field is show on geographic coordinates, which can be longitude, latitude, vertical hybrid-pressure level (for zonal mean fields, such as ozone, temperature and age-of-air).</p> <p>The dimensions in each field are:</p> <p>lat --> latitude</p> <p>lon --> longitude</p> <p>lev --> hydrid pressure level</p> <p>time --> months of the simulation</p> <p>The output of the total column ozone from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 1-4 and Figure S2 in the supplement are in files:</p> <p>toz_A_ERAI.nc</p> <p>toz_B_ERA5.nc</p> <p>The output of the stratospheric column ozone (SCO) in Figure S1 in the supplement are in the file (levels1-3 are SWOOSH, B_ERA5 and A_ERAI SCO data, respectively):</p> <p>sco_SWOOSH_A_ERAI_B_ERA5.nc</p> <p>The output of zonal mean ozone profiles from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 5-7, 9 and Figures S3-4 are in files:</p> <p>O3_mm_A_ERAI.nc</p> <p>O3_mm_B_ERA5.nc</p> <p>The output of zonal mean temperature from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figure 8 are in files:</p> <p>te_mm_A_ERAI.nc</p> <p>te_mm_B_ERA5.nc</p> <p>The output of zonal mean age-of-air from the TOMCAT/SLIMCAT simulations forced with ERA-Interim and ERA5 for Figures 10-12 are in files:</p> <p>Age_mm_A_ERAI.nc</p> <p>Age_mm_B_ERA5.nc</p> <p>The output of the zonal mean ozone, temperature and age-of-air from the ERA5.1 reanalysis corrected simulations during the period from 2000 to 2006 in all Figures above using ERA5 are in files:</p> <p>ERA5_1_O3_2000_18.nc</p> <p>ERA5_1_te_2000_18.nc</p> <p>ERA5_1_Age_2000_18.nc</p> <p> </p>
Scaling protein-water interactions in the Martini 3 coarse-grained force field to simulate transmembrane helix dimers in different lipid environments
<p>This dataset contains molecular dynamics (MD) trajectories used for preparation of the following manuscript: <br> "Scaling protein-water interactions in the Martini 3 coarse-grained force field to simulate transmembrane helix dimers in different lipid environments". </p>
MD simulation trajectory of a POPC/POPS (4:1) bilayer with 715mM CaCl2, Berger force field for lipids, scaled charges for Ca2+ and Cl-
<p>MD simulation trajectory of a POPC/POPS (4:1) bilayer with 715 mM CaCl2 (104 POPC, 24 POPS, 26 POPS, 4306 WAT, 72 Ca2+, 112 Cl-). Additional Ca2+ cations added to neutralize the negative charge of POPS (leading to total Ca2+ concentration of 919 mM). Berger force field for lipids, scaled charges employed for calcium and chloride ions. Gromacs 4.5.5, T=310K, 300 ns trajectories were calculated with the last 100 ns stored here.</p> <p>Used in (see therein also a detailed description of ion scaling):</p> <p>A. Melcrova, S. Pokorna, S. Pullanchery, M. Kohagen, P. Jurkiewicz, M. Hof, P. Jungwirth, P. S. Cremer, L. Cwiklik, The complex nature of calcium cation interactions with phospholipid bilayers<br> Scientific Reports 2016, 6, 38035.<br> DOI: 10.1038/srep38035</p>
MD simulation trajectory of a POPC bilayer with 716mM CaCl2, Berger force field for lipids, scaled charges for Ca2+ and Cl-
<p>MD simulation trajectory of a POPC bilayer with 716 mM CaCl2 (128 POPC, 26 POPS, 4308 WAT, 56 Ca2+, 112 Cl-). Berger force field for lipids, scaled charges employed for calcium and chloride ions. Gromacs 4.5.5, T=310K, 200 ns trajectories were calculated with the last 100 ns stored here.</p> <p>Used in (see therein also a detailed description of ion scaling):</p> <p>A. Melcrova, S. Pokorna, S. Pullanchery, M. Kohagen, P. Jurkiewicz, M. Hof, P. Jungwirth, P. S. Cremer, L. Cwiklik, The complex nature of calcium cation interactions with phospholipid bilayers<br> Scientific Reports 2016, 6, 38035.<br> DOI: 10.1038/srep38035</p>
MD simulation trajectory of a POPC/POPS (4:1) bilayer with 102mM CaCl2, Berger force field for lipids, scaled charges for Ca2+ and Cl-
<p>MD simulation trajectory of a POPC/POPS (4:1) bilayer with 102 mM CaCl2 (104 POPC, 24 POPS, 26 POPS, 4306 WAT, 24 Ca2+, 16 Cl-). Additional Ca2+ cations added to neutralize the negative charge of POPS (leading to total Ca2+ concentration of 306 mM). Berger force field for lipids, scaled charges employed for calcium and chloride ions. Gromacs 4.5.5, T=310K, 300 ns trajectories were calculated with the last 100 ns stored here.</p> <p>Used in (see therein also a detailed description of ion scaling):</p> <p>A. Melcrova, S. Pokorna, S. Pullanchery, M. Kohagen, P. Jurkiewicz, M. Hof, P. Jungwirth, P. S. Cremer, L. Cwiklik, The complex nature of calcium cation interactions with phospholipid bilayers<br> Scientific Reports 2016, 6, 38035.<br> DOI: 10.1038/srep38035</p>
MD simulation trajectory of a POPC bilayer with 100mM CaCl2, Berger force field for lipids, scaled charges for Ca2+ and Cl-
<p>MD simulation trajectory of a POPC bilayer with 100 mM CaCl2 (128 POPC, 26 POPS, 4452 WAT, 8 Ca2+, 16 Cl-). Berger force field for lipids, scaled charges employed for calcium and chloride ions. Gromacs 4.5.5, T=310K, 200 ns trajectories were calculated with the last 100 ns stored here).</p> <p>Used in (see therein also a detailed description of ion scaling):</p> <p>A. Melcrova, S. Pokorna, S. Pullanchery, M. Kohagen, P. Jurkiewicz, M. Hof, P. Jungwirth, P. S. Cremer, L. Cwiklik, The complex nature of calcium cation interactions with phospholipid bilayers<br> Scientific Reports 2016, 6, 38035.<br> DOI: 10.1038/srep38035</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.