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91 results for “Molecular dynamic study”
Growth of hexagonal boron nitride from molten nickel solutions: a reactive molecular dynamics study
<p>Authors: Amin Ahmadisharaf and Jeffrey Comer</p> <p><br>Publication: Amin Ahmadisharaf, Bin Liu, James H. Edgar, and Jeffrey Comer (2025) Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study. ACS Applied Materials & Interfaces. <a href="https://doi.org/10.1021/acsami.4c16991">doi.org/10.1021/acsami.4c16991</a></p> <p>Funding: Department of Energy Office of Science, grant DE-SC0021264, <a>https://pamspublic.science.energy.gov/WebPAMSExternal/Interface/Common/ViewPublicAbstract.aspx?rv=5e6ffff5-0daa-47e8-a3d0-20f594b7bfb8&rtc=24&PRoleId=10</a></p> <p>**************************************</p> <p>This data set for the manuscript entitled "Growth of Hexagonal Boron Nitride from Molten Nickel Solutions: A Reactive Molecular Dynamics Study" includes all files needed to run and analyze the simulations described in the this manuscript in the molecular dynamics software LAMMPS, as well as the output of the simulations. The files are organized into directories corresponding to the figures of the main text. They include force field parameter files (in ReaxFF format), LAMMPS configuration files (*.in), ReaxFF control files (*.control), LAMMPS log files (*.log), and LAMMPS output including restart files (in binary LAMMPS format) and trajectories in dcd format (downsampled to 12.5 or 25 ps per frame) and also PDB and PSF files are useful for visualization with VMD. Analysis is performed by python and shell scripts (Bash-compatible) that call VMD Tcl scripts or python scripts. These scripts and their output are also included.</p> <p>The species analysis is performed by the VMD Tcl script "Figure3/analysis/count_hBN_species_NNB_BN.tcl" using the parameters given in "Figure3/analysis/doCount.sh".</p> <p>The directory contents are as follows.</p> <p>--------------------------------------------------------------<br>Figure-1: Parallel tempering simulations of the boron-nickel system and calculation of the boron concentration along the z-dimension of the nickel slab.</p> <p>The analysis of the boron concentration profile is performed by the VMD Tcl scripts calcRatioZRef.tcl and calcConcZRef.tcl using the parameters given in "Figure1/analysis/step3_conc_profile.sh". Also, the reorganization of the parallel tempering trajectories into frames at a single temperature is performed by the VMD Tcl script extractReplicaFrames.tcl based on "Figure1/analysis/step1_sort_frames.sh".</p> <p><br>--------------------------------------------------------------<br>Figure-2: Simulation of hBN sheet growth at 1750 K and calculation of largest cluster.</p> <p><br>--------------------------------------------------------------<br>Figure-3: Simulations of different boron-to-nickel ratios at varying nitrogen pressures at 1750 K and the calculation of the largest hBN cluster formed under different scenarios. The suffixes "liu", "long_liu", and "low_liu" correspond to pressures of 100.0, 50.0, and 25.0 atm respectively.</p> <p>The analysis of hBN clusters is performed by the VMD Tcl script "Figure3/analysis/" using the parameters given in "Figure3/analysis/doCount.sh". The related simulations files and outputs for panel A in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-4: Recognition and counting the different boron-nitrogen motifs in the simulation was performed in Figure 2.</p> <p>The related simulations files and outputs for panel B and C in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-5: Simulation of the temperature effect on hBN growth, and recognition and counting of the different boron-nitrogen motifs at 1750, 1800, 1900, 2000, 2200, and 2700 K.. </p> <p><br>--------------------------------------------------------------<br>Figure-6: Recognition of existing motifs for nitrogen atoms in the growth path of hBN and calculation of the probabilities of transitions between different motifs across all nitrogen atoms.</p> <p>The related simulations files and outputs for all panels in this figure are located in Figure 5 directory.</p> <p><br>--------------------------------------------------------------<br>Figure-7: Comparing the ReaxFF and ab initio simulations of small B-N motifs(B--N--B and B--N) in a nickel slab and calculation of bond lengths and angle values.</p> <p><br>--------------------------------------------------------------<br>Figure-8: Diffusion simulations of four different systems at 1800 K: nickel with a single B atom, nickel with a single N atom, nickel with a free B-N-B molecule, and nickel with a small hBN sheet and Mean Squared Displacement (MSD) values were calculated and compared to assess the surface mobility of the different particles.</p> <p> </p>
Broadband Dielectric Spectroscopy Study of Biobased Poly(alkylene 2,5-furanoate)s' Molecular Dynamics
<p><strong>Related publication:</strong><br> Soccio, M.; Martínez-Tong, D.E.; Guidotti, G.; Robles-Hernández, B.; Munari, A.; Lotti, N.; Alegria, A. Broadband Dielectric Spectroscopy Study of Biobased Poly(alkylene 2,5-furanoate)s’ Molecular Dynamics. <em>Polymers</em> 2020, <em>12</em>, 1355.<br> <a href="https://doi.org/10.3390/polym12061355">10.3390/polym12061355</a></p> <p><strong>EUSMI proposal codes:</strong><br> E171100040, E171100043</p>
Data for the publication "Sodium Triflate Water-in-Salt Electrolyte in Advanced Battery Applications: A First-principles Based Molecular Dynamics Study"
<p>The datasets 'CONTCAR_aiMLMD' and 'CONTCAR_AIMD' represent the final structures obtained from the aiMLMD and AIMD simulations, respectively. These simulations were conducted using VASP at T=333K and c=9.25 m.</p> <p>The datasets 'NP.rdf' and 'MSD_NP.xlsx' represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. The associated MD simulation was performed using a nonpolarizable force field in the LAMMPS package at T=333K and c=9.25 m. The file 'dataNP.lmp' includes the initial configuration for this simulation. The GROMOS parameters were employed for LJ interactions of sodium and all other force field parameters were set according to Table 1 in the manuscript.</p> <p>The datasets 'P.rdf' and 'MSD_P.xlsx,' respectively, represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. These data were obtained employing the Drude oscillator model in the LAMMPS package at T=333K and c=10 m. The file 'dataP.lmp' includes the initial configuration for this simulation. The simulation was conducted using the optimal force field parameters 'Sys. 1,' as described in table 3 of the manuscript.</p> <p>The second column in the files 'NP.rdf' and 'NP.rdf' represents the distance from sodium. The subsequent odd columns display the radial distribution functions for the Na-C, Na-F, Na-S, Na-O, Na-Na, Na-Hw, and Na-Ow pairs, while the even columns present the coordination numbers for the same atom pairs.</p>
Intrinsic and apparent slip at gas-enriched liquid-liquid interfaces: a molecular dynamics study
<p>- "sl1.dat" : text file with data slip length vs number of gas atoms for k_gas = 1.0</p> <p>- "sl5.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.5</p> <p>- "sl25.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.25</p> <p>- "sl125.dat" : text file with data slip length vs number of gas atoms for k_gas = 0.125</p> <p>- "plotsl.plt": gnuplot script to plot slip lengths data and obtain figure 5a of the article</p> <p>- "dg.dat": data for solubilities in kbT units from figure 3 of the article</p> <p>- "3600gask0125.xyz": trajectory file in xyz format for the system with k_gas= 0.125 and 3600 gas atoms</p> <p>- "3600.data": starting configuration for k_gas = 0.125 and 3600 gas atoms in restart.data format for lammps</p> <p>- "in.shear": lammps input script to run the shear simulation for the system with k_gas = 0.125 and 3600 gas atoms starting from configuration store in "3600.data" file</p> <p> </p>
A molecular dynamics study of adenylyl cyclase: the impact of ATP and G-protein binding
<p>Adenylyl cyclases (ACs) catalyze the biosynthesis of cyclic adenosine monophosphate (cAMP) from adenosine triphosphate (ATP) and play an important role in many signal transduction pathways. The enzymatic activity of ACs is carefully controlled by a variety of molecules, including G-protein subunits that can both stimulate and inhibit cAMP production. Using homology models developed from existing structural data, we have carried out all-atom, microsecond-scale molecular dynamics simulations on the AC5 isoform of adenylyl cyclase and on its complexes with ATP and with the stimulatory G-protein subunit Gsα. The results show that both ATP and Gsα binding have significant effects on the structure and flexibility of adenylyl cyclase. New data on ATP bound to AC5 in the absence of Gsα notably help to explain how Gsα binding enhances enzyme activity and could aid product release. Simulations also suggest a possible coupling between ATP binding and interactions with the inhibitory G-protein subunit Gαi.</p> <p>All-atom molecular dynamics simulations were performed with the GROMACS 5 package. The simulations were carried out in an NTP ensemble at a temperature of 310 K and a pressure of 1 bar using a Bussi velocity-rescaling thermostat (t<sub>T</sub> = 1 ps) and a Parrinello-Rahman barostat (t<sub>P</sub> = 1 ps). We provide the atomistic trajectories of the following 6 systems after 400 ns of equilibration:</p> <ul> <li>AC5</li> <li>AC5+ATP</li> <li>AC5+Gsα</li> <li>AC5+ATP+Gsα</li> <li>AC5+FOK</li> <li>AC5+ATP+FOK</li> </ul> <p>In each trajectory, the frames are saved each 20 ps.</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 80 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study
<p>The data necessary to independently reproduce the MD simulations and the first part of the simulation trajectories reported in the paper "<strong>How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study</strong>", by Lemmens et al.</p> <p>Due to Zenodo data deposition limits, every 10th frame of the MD simulation trajectories is included. Due to Zenodo deposition limits, MD trajectory files for this paper are also available at 10.5281/zenodo.14260246.</p>
Data of curvature model for the study of nanoparticle size effects on amyloid fibril stability and molecular dynamics simulations data
<p>The data provided refer to our published article:</p> <p>T. John, J. Adler, C. Elsner, J. Petzold, M. Krueger, L.L. Martin, D. Huster, H.J. Risselada, B. Abel, Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation, J. Colloid Interface Sci. 622 (2022), 804–818. <a href="https://doi.org/10.1016/j.jcis.2022.04.134">https://doi.org/10.1016/j.jcis.2022.04.134</a></p> <p>This article is accompanied by a 'Data in Brief' article that explains in more detail the use of the curvature model and our molecular dynamics (MD) simulations:</p> <p>T. John, L.L. Martin, H.J. Risselada, B. Abel, Curvature model for nanoparticle size effects on peptide fibril stability and molecular dynamics simulation data, Data Brief 45 (2022), 108598. <a href="https://doi.org/10.1016/j.dib.2022.108598">https://doi.org/10.1016/j.dib.2022.108598</a></p>
Isomorph Invariant Dynamic Mechanical Analysis: A Molecular Dynamics Study
<p>This data set contains the data required to reproduce most of the figures in our paper, [arXiv:2204.06962] which will soon be submitted to a journal. Abstract of the paper:</p> <p>We simulate dynamic mechanical analysis experiments for the Kob-Andersen binary Lennard-Jones system. For this, the SLLOD algorithm with time-dependent strain rates is applied to give a sinusoidally varying strain at different densities and temperatures. The starting point is a temperature scan at a fixed reference density. Isomorph theory predicts that for other densities corresponding temperatures can be identified at which the mechanical properties are unchanged when scaled appropriately. We determine the isomorphically equivalent temperatures by analysing how<br> particle forces change upon scaling configurations to the new density. Loss moduli expressed in suitable reduced units are compared for isomorphic state points. When plotted against the unscaled temperatures, these reduced loss curves are observed to collapse indicating the validity of isomorph theory for dynamic mechanical analysis experiments. Two different methods to determine isomorphic temperatures are considered. While one of them breaks down for the largest density rescalings considered in this study, the other one is still applicable in this region. The decorrelation of force vectors upon rescaling is investigated as a possible origin of this effect. Our results demonstrate that the simplification of the phase diagram entailed by isomorph theory for a wide class of system<br> is relevant also for the mechanical properties of glasses.</p> <p> </p> <p> </p>
A dynamical view of protein-protein complexes: studies by molecular dynamics simulations
<p>All-atom MD simulations generated for the manuscript "A dynamical view of protein-protein complexes: studies by molecular dynamics simulations". Eight binary protein-protein complexes from the Docking benchmark and the Affinity benchmark are studied in this work: 2OOB (an ubiquitin/ubiquitin ligase complex), 1AY7 (a ribonuclease Sa/barstar complex), 1BRS (a barnase/barstar complex), 3SGB (a proteinase B/inhibitor), 1EMV (a colicin/immunity protein complex), 1PVH (complex between Interleukine 6 receptor and leukemia inhibitory factor), 1GCQ (Vav/GRB2 SH3 domains complex) and 1AK4 (cyclophilin/HIV capsid complex)</p> <p>Each folder for a binary complex is organised as followed:</p> <p>- in <strong>A</strong> and <strong>B</strong> there are the dry MD simulations for the unbound proteins</p> <p>- in <strong>complex</strong> there are two folders (<strong>without_water</strong> and <strong>water</strong>) where the dry simulation and the simulation with water molecules are provided</p>
Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites’ mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale. </p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages<strong>:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3]</p> <p>Post-processing Matlab R2019b</p> <p>Evaluation of polymer entanglements with Z1-Algorithm [4]</p> <p> </p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, <strong>2022</strong>, 16.</p> <p>This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information.</p> <p><strong>Content:</strong></p> <p>supplementary material:</p> <p>supplementary_information.pdf</p> <p>data:<br> folder names vary depending on the context, explained in the following:</p> <p> </p> <p>01_matrix</p> <ul> <li> <p>01_equilibration<br> sample equilibration to different temperatures<br> nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> <li> <p>batch_ID: 2-5 </p> </li> </ul> </li> <li> <p>02_temperature_dependence<br> uniaxial tension simulations to identify temperature dependence<br> nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> </ul> </li> <li> <p>03_directional_dependence<br> uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence<br> nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-5</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>04_rate_dependence<br> uniaxial tension simulations to identify strain rate dependence<br> nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>05_cyclic_loading<br> sinusoidal uniaxial deformation<br> nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>06_relaxation<br> relaxation subsequent to time-proportional deformation<br> nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>07_simple_shear<br> time-proportional simple shear deformation with different strain rates<br> nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID></p> <ul> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>08_large_deformation<br> uniaxial deformation up to 100% strain<br> nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>max_strain: 1</p> </li> </ul> </li> </ul> <p>02_filler</p> <ul> <li> <p>01_Silica_equilibration<br> sample equilibration</p> </li> <li> <p>02_time_proportional<br> time-proportional uniaxial and simple shear tests<br> nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate></p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>max_strain: 0.1</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> </ul> </li> <li> <p>03_time_periodic<br> time-periodic uniaxial and simple shear tests<br> nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate></p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_amplitude: 0.025</p> </li> </ul> </li> </ul> <p>03_composite</p> <ul> <li> <p>01_equilibration<br> sample equilibration<br> nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID></p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>02_uniaxial-tension<br> uniaxial tension simulations<br> nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID></p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>03_filler-maxtrix-adhesion<br> equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension<br> nomenclature: see above</p> </li> <li> <p>04_IP_equilibration<br> equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5<br> nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>filler_radius: 0 (neat), 2.5, 5.0, 7.5</p> </li> <li> <p>filler_number: 0 (neat), 1</p> </li> <li> <p>batchID: 1-20</p> </li> </ul> </li> </ul> <p> </p> <p> </p> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p> </p> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><br> </p> <p><strong>References</strong>:</p> <p>[1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, <em>Express Polymer Letters</em>, <strong>2022</strong>, 16.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations
<p># *"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"*</p> <p>The input files and data used for the paper *"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"* are separated in the different folders depending stage they belong to.</p> <p>## Folders</p> <p> 1. **01_inputs:** The MD trajectory of DhaA used (only protein atoms present)<br> 2. **02_sliced_trajectory:** The CAVER3 results for the sliced trajectory (eight parts)<br> 3. **03_sliced_filtered:** Filtered CAVER3 results and results from the divide-and-conquer approach<br> 4. **04_full_trajectory:** The CAVER3 results for the full trajectory analysis<br> 5. **05_guided_example:** Guided example for the divide-and-conquer approach<br> </p>
Supplementary data for "Molecular-scale thermally activated fractures in methane hydrates: A molecular dynamics study"
<p>In this dataset you can find</p> <p>- a data sample that can be used to confirm the plots in the paper. This can be found in the folder "data_sample". Each folder inside "data_sample" is one simulation. It contains the thermodynamic output from the simulation (log.lammps), and the input script (mw_hydrate_pennycrack.in) and input data (s1_unit_cell_mw.data and water_methane_hydrate.sw) that enables rerunning the simulation using LAMMPS.</p> <p>- A custom LAMMPS region, region_ellipsoid. This has to be compiled into LAMMPS in order to create the systems that we simulate.</p> <p>- A python script, plot_data.py, that shows how to extract the relevant data from the lammps log files, which enables the partial reproduction of figure 3 in the paper. See instructions below for requirements to use this script.</p> <p> </p> <p>"region_ellipsoid" and "data_sample" are in zip containers. In order to use them, please unzip them and leave the resulting folders in the same directory as this README file.</p> <p> </p> <p>Installation instructions to make "plot_data.py" work (assuming you already have numpy and matplotlib):</p> <p>> pip3 install git+https://github.com/henriasv/regex-file-collector.git</p> <p>> pip3 install git+https://github.com/henriasv/lammps-logfile.git</p> <p> </p> <p>If this does not work, please contact Henrik Andersen Sveinsson, henriasv@fys.uio.no</p>
Initial Structures of PKM1/M2 proteins for AMOEBA Molecular Dynamics studies (xyz Tinker format)
<p>Here are presented our initial structures of PKM1/M2 (solvated and neutralized) for the different states to initiate molecular dynamics in AMOEBA force field.</p> <p>Those are represented in xyz Tinker format and come from their respectives PDB crystal structure after extraction of the unwanted ligands :</p> <p>3SRF for PKM1,</p> <p>1ZJH for monomer PKM2,</p> <p>6B6U for dimer PKM2,</p> <p>3SRH for free-tetramer PKM2,</p> <p>3SRD for tetramer PKM2 bound to FBP,</p> <p>3U2Z for tetramer PKM2 bound to TEPP-46.</p>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
Interaction of biomolecules with anatase, rutile and amorphous TiO2 surfaces: A molecular dynamics study / supplement
<ul> <li>S1 File - Short videos representing the interaction of the 6 different biomolecules (KGD, KRSR, LGD, LRSR, RGD and RSR) with the 3 different TiO<sub>2</sub> surface (anatase, amorphous and rutile).</li> <li>S2 File - A representative trajectory file of the LRSR peptide and amorphous TiO<sub>2</sub> surface MD simulation without the ions and water molecules.</li> <li>S3 File - Force-distance curve data for the binding energy calculations.</li> </ul>
Input files for the MD simulations and free energy calculations for the article "Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions"
<p>Article:<em> </em><a href="https://pubs.acs.org/doi/10.1021/acs.jpcb.1c04818">J. Phys. Chem. B. 125, 9357–9371 (2021) [DOI: 10.1021/acs.jpcb.1c04818]</a></p> <p>The structures of the homopolymers and copolymers simulated are shown in Figures 1 and S1 and Tables 2 and 3. All-atom MD simulation was carried out using GROMACS, and this repository provides the input files with the GAFF/RESP force and initial coordinate files. The free energy of water dissolution was obtained with <a href="https://sourceforge.net/projects/ermod/">ERmod</a>, and the input files for the free-energy calculations are also contained. See the README files for details.</p>
Supplementary data for "Molecular dynamics study of confined water in the periclase-brucite system under conditions of reaction-induced fracturing"
<p>In this dataset you can find sample data from periclase and brucite simulations and python scripts that can be used to confirm the plots in the paper.</p> <ul> <li>"bruciteSimualtions" and "periclaseSimualtions" contains the simulations where the mineral in contact with water is either brucite or periclase. Within each of these two folders, there are two subfolders, "waterProperties" and "waterThickness". <ul> <li>The data in "waterProperties" is used to calculate water properties. Each folder inside "simulations" represent one simulation.</li> <li>The data in "waterThickness" is used to calculate the change in water film thickness with time under different conditions. The simulations are run for either 3 ns or 10 ns. Each folder inside "simulations_Xns" represent one simulation.</li> </ul> </li> <li>For each folder containing one simulations, we provide: <ul> <li>NAME.run: The input script</li> <li>NAME.data: The input data</li> <li>job.sh: Script to run the simulation</li> <li>log.lammps: Thermodynamic output from the simulation</li> <li>Note that the pressures given in the folder names and in the simulations are in atm, not MPa.</li> </ul> </li> </ul> <p> </p> <ul> <li>"bruciteSimulations" and "periclaseSimualtions" are in zip containers. In order to use them, please unzip them and leave the resulting folders in the same directory as this README file. </li> </ul> <p> </p> <ul> <li>The python scripts shows how to extract the relevant data from the lammps log files, which enables reproduction of the figures in the paper. See instructions below to use the scripts.</li> </ul> <p><br> Installation instructions to make the python plot scripts working, assuming you already have numpy and matplotlib:</p> <p>> pip3 install git+https://github.com/henriasv/regex-file-collector.git</p> <p>> pip3 install git+https://github.com/henriasv/lammps-logfile.git<br> </p> <p>If this does not work, please contact Marthe Grønlie Guren, m.g.guren@geo.uio.no</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 70 wt% C12E5, T=298K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 13C-1H solid-state NMR and molecular dynamics simulations"
<p>Simulation trajectories for the article "Molecular conformation and bilayer pores in a nonionic surfactant lamellar phase studies with 1H-13C solid-state NMR and molecular dynamics simulations" Langmuir 2014, 30 (2), pp 461–469 http://dx.doi.org/10.1021/la404684r</p> <p>System: 70 wt% C12E5, T=320K</p> <p>Other files available: http://dx.doi.org/10.6084/m9.figshare.861071</p>
ScienceDex guides
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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.