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306 results for “mechanical modeling”
Four-Chamber Human Heart Model for the Simulation of Cardiac Electrophysiology and Cardiac Mechanics
<p><strong>Changes in version 1.1 compared to version 1.0:</strong></p> <ul> <li>Ventricular fiber orientation changed to 66° on the endocardial and −41° on the epicardial surface</li> <li>Electrophysiology mesh was resampled</li> <li>Updated material tags in EP mesh</li> <li>Further details can be found in the <a href="https://github.com/KIT-IBT/CardioMechanics/tree/main">CardioMechanics GitHub repository</a> new reference paper:</li> </ul> <blockquote> <p>Gerach, T.; Loewe, A. Differential effects of mechano-electric feedback mechanisms on whole-heart activation, repolarization, and tension. <em>The Journal of Physiology</em> <strong>2024. </strong>https://doi.org/10.1113/JP285022 </p> </blockquote> <p>This repository contains a four-chamber model of the human heart which is ready to use for simulations of cardiac electrophysiology and cardiac mechanics problems. When using this dataset, please also cite the accompanying paper</p> <blockquote> <p>Gerach, T.; Schuler, S.; Fröhlich, J.; Lindner, L.; Kovacheva, E.; Moss, R.; Wülfers, E.M.; Seemann, G.; Wieners, C.; Loewe, A. Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. <em>Mathematics</em> <strong>2021</strong>, <em>9</em>, 1247. https://doi.org/10.3390/math9111247</p> </blockquote> <p>The cardiac anatomy was manually segmented from magnetic resonance imaging (MRI) data of a 33 year old male volunteer. The volunteer provided informed consent and the study was approved by the IRB of Heidelberg University Hospital (Fritz et al., 2014).<br>The MRI data were acquired using a 1.5 T MR tomography system and consist of a static whole heart image stack taken during diastasis as well as time-resolved images in several long and short axis slices. Based on the segmentation, we first labeled the atria and the ventricles. The geometry was extended by a representation of the mitral valve, the tricuspid valve, the aortic valve and the pulmonary valve. Additionally, we closed the endo- and epicardial surfaces of the atria and added truncated pulmonary veins, vena cavae as well as the ascending aorta and pulmonary artery. Furthermore, we added a concentric layer of tissue around the entire heart which phenomenologically represents the influence of the pericardium and the surrounding tissue.</p> <p>Two tetrahedral meshes were created using Gmsh (Geuzaine et al., 2009): (1) the mechanical reference domain (<strong>M.vtu</strong>) with 128,976 elements (on average 3.17 mm edge length) and (2) the electrophysiological reference domain (<strong>EP.vtu</strong>) as a subset of M with 50,058,295 elements (on average 0.4 mm edge length).<br>We used rule-based methods to assign the myofiber orientation on EP: Wachter et al. (2015) was used for the atria and Bayer et al. (2012) for the ventricles. The fiber angle in the ventricles was chosen as +60° and -60° on the endocardial and epicardial surface, respectively. The sheet angle was set to -65° on the endocardium and 25° on the epicardium. Github repositories to these fiber generation tools are given in the sidebar. All geometry files are given in millimeter (mm).</p> <ul> <li><strong>Data:</strong><br>We provide time resolved data evaluated from cine MRI data, which can be used for model calibration. <ul> <li>Wall thickening (<strong>17AHA_WT.txt</strong>) / fractional wall thickening (<strong>17AHA_fractionalWT.txt</strong>) in the 17 AHA segments of the left ventricle</li> <li>Atrioventricular plane displacement (<strong>AVPD.txt</strong>) and velocity (<strong>AVPV.txt</strong>) as well as the displacement of all tracked points used for AVPD calculation (<strong>AVPD_trackedPoints.txt</strong>)</li> <li>Left and right ventricular volume (<strong>Volume_LV_RV.txt</strong>). RV volume is only available for end-diastole and end systole.</li> </ul> </li> <li><strong>Surfaces:</strong><br>This directory contains *.stl files with triangulated surfaces on which boundary conditions can be applied. <ul> <li><strong>cavityXX.stl</strong>: blood volume of the LV, RV, LA, RA</li> <li><strong>epicard.stl</strong>: the whole epicardium</li> <li><strong>outerPeri.stl</strong> and <strong>outerTrunks.stl</strong>: surfaces for Dirichlet boundary conditions</li> <li><strong>master.stl </strong>and <strong>slave.stl</strong>: surfaces used for the frictionless contact problem described in Fritz et al. (2014)</li> </ul> </li> <li><strong>TetGen:</strong><br>Contains the geometry <strong>M.vtu</strong> in the TetGen file format. T4 mesh with 4 node tetrahedrons and 3 node triangles. <ul> <li>.bases: fiber, sheet, and normal orientation at quadrature points of all elements</li> <li>.node: vertex coordinates</li> <li>.ele: list of tetrahedra</li> <li>.sur: list of triangles</li> </ul> </li> <li><strong>EP.vtu:</strong><br>Contains the cell arrays Fiber and Material.</li> <li><strong>M.vtu:</strong><br>Contains the cell arrays Fiber, Sheet, Sheetnormal, Material, and Label.</li> <li><strong>LabelIDs.txt:</strong><br>List of Label and Material identification numbers and corresponding anatomical structures.</li> </ul> <p> </p>
Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).
<p>Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).</p> <p>This folder contains the files needed to start each of the models described in the paper. The files were created using MOE 2020 software made by Chemical Computing Group and run on NAMD2.</p> <p>The models identifiers in the paper correspond to the following terms in the code:</p> <p>Substrate: "13_5_coa"</p> <p>Product: "13_5_coa_desat_fe3"</p> <p>Apoprotein: "13_5_no_ligand"</p> <p>Saturated Lipid: "13_5_nocoa"</p> <p>Desaturated Lipid: "13_5_nocoa_desat_fe3"</p> <p>CoA model: "13_5_coa_nolipid"</p> <p>Substrate-waterbox model: "13_5_coa_waterbox"</p> <p>Saturated Lipid-waterbox: "13_5_nocoa_waterbox"</p>
DMS multiphase chemistry mechanism and model results
<p><strong>Open Access DMS multiphase chemistry mechanism</strong></p> <p>This repository contains the complete DMS multiphase chemistry mechanism developed and applied in Wollesen de Jonge et al. (2021). The DMS multiphase mechanism is located in the folder named <strong>DMS_chemistry</strong>. The executable mechanism consist of a number of Fortran f90 files, which were generated with the kinetic pre-processor (KPP) (Damian et al., 2002) using the KPP input file <em>DMSchem.def</em> and <em>DMSchem.kpp</em> file. Both the executable Fortran code and the KPP input files are stored in the subfolder <strong>DMS_multiphase_chem</strong>. The <em>DMSchem.def</em> file list all reactions and reaction rates in the DMS multiphase chemistry mechanism similar to the supplementary Tables S1 in Wollesen de Jonge et al. (2021). The executable DMS multiphase chemistry mechanism consist of the following Fortran f90 files:</p> <p><em>DMSchem_Main.f90</em></p> <p><em>DMSchem_Function.f90</em></p> <p><em>DMSchem_Initialize.f90</em></p> <p><em>DMSchem_Integrator.f90</em></p> <p><em>DMSchem_Jacobian.f90</em></p> <p><em>DMSchem_JacobianSP.f90</em></p> <p><em>DMSchem_LinearAlgebra.f90</em></p> <p><em>DMSchem_mex_Fun.f90</em></p> <p><em>DMSchem_mex_Jac_SP.f90</em></p> <p><em>DMSchem_Model.f90</em></p> <p><em>DMSchem_Monitor.f90</em></p> <p><em>DMSchem_Parameters.f90</em></p> <p><em>DMSchem_Precision.f90</em></p> <p><em>DMSchem_Rates.f90</em></p> <p><em>DMSchem_Util.f90</em></p> <p><em>DMSchem_Global.f90</em></p> <p>These f90-files can be linked and compiled with gfortran using the provided <em>Makefile</em>. </p> <p>The subfolder <strong>photolysis</strong> contain vectors with absorption cross sections (cs), quantum yields (qy) and the spectral actinic flux of the UV-lamps in the AURA chamber.</p> <p>A simplified model setup is provided to illustrate how the DMS-multiphase chemistry routines can be run. The DMS multiphase chemistry mechanism is called and run from a program named <em>main.f90</em>.</p> <p>In the <em>main.f90</em> program the temperature, humidity, pressure and initial concentrations of all gas and aqueous phase species in the DMS multiphase chemistry are declared.</p> <p>After this the main program call the subroutines <em>getKVALUES</em> and <em>getJVALUES</em> from the Fortran module <em>reaction_rates.f90</em>.</p> <ul> <li><em>getKVALUES</em> calculates a number of complex reaction rates (mainly pressure dependent three-body reactions).</li> <li><em>getJVALUES</em> calculates all gas phase photolysis rates in the DMS multiphase chemistry mechanism using the absorption cross sections, quantum yields and the spectral actinic flux files stored in the <strong>photolysis</strong> subfolder</li> </ul> <p>The <em>main.f90</em> program saves the concentration of all species in a file called <em>conc.dat</em>, the time step vector (<em>time.dat</em>) and all species names in <em>SPC_NAMES.dat.</em></p> <p>A short Matlab script called <em>plot_concentrations.m</em> is provided to illustrate how the concentrations of all species listed in <em>SPC_NAMES.dat</em> can be plotted along the saved time vector.</p> <p>The simplified model (only used for demonstration purpose) can be compiled and executed with GFortran using the provided <em>Makefile</em> by typing the following commands in the command line (terminal):</p> <p>make</p> <p>./main.exe</p> <p> </p> <p><strong>Stored model results presented in Wollesen de Jonge et al. (2021)</strong></p> <p>We have saved all model data from each simulated smog chamber experiment and atmospheric relevant base case and sensitivity run presented in Wollesen de Jonge et al. (2021) in the form of 'OutputTable' files.</p> <p>The columns in the tables related to the DMS smog chamber experiments are classified as follows:</p> <ul> <li><em>1 time</em> [h] (simulation time starting from -1 h hour and ending at 15 h, time = 0 h is defined as the time when the UV-lights were turned on in the AURA smog chamber).</li> <li><em>2 PN_1.7nm</em> [#/cm^3] (Total particle number concentration of particles 1.7 nm in diameter).</li> <li><em>3 PN_2.5nm</em> [#/cm^3] (Total particle number concentration of particles 2.5 nm in diameter)</li> <li><em>4 PN_10nm</em> [#/cm^3] (Total particle number concentration of particles 10 nm in diameter)</li> <li><em>5 PM_SO4</em> [μg/m^3] (Sulfate particle mass)</li> <li><em>6 PM_CH3SO3</em> [μg/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li><em>7 PM_NH4</em> [μg/m^3] (Ammonium particle mass)</li> <li><em>8 DMS</em> [ppb<sub>v</sub>] (Dimethyl sulfide gas phase concentration)</li> <li><em>9 O3</em> [ppb<sub>v</sub>] (Ozone gas phase concentration)</li> <li><em>10 PV</em> [μm^3/cm^3] (Total particle volume concentration)</li> <li><em>11 PM</em> [μg/m^3] (Total particle mass concentration)</li> <li><em>12 NH3</em> [ppb<sub>v</sub>] (Ammonia gas phase concentration)</li> <li><em>13 MSIA</em> [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li><em>14 SO2</em> [ppb<sub>v</sub>] (Sulfur dioxide gas phase concentration)</li> <li><em>15 DMSO</em> [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li><em>16 HPMTF</em> [#/cm^3] (Hydroperoxymethyl thioformate gas phase concentration)</li> <li><em>17 H2O2</em> [ppb<sub>v</sub>] (Hydrogen peroxide gas phase concentration)</li> <li><em>18 HO2</em> [#/cm^3] (Hydroperoxyl radical gas phase concentration)</li> <li><em>19 OH</em> [#/cm^3] (Hydroxyl radical gas phase concentration)</li> <li>20-219<em> dN/dlogDp</em> [#/m^3] (Particle number size distributions)</li> </ul> <p> </p> <p>The header line, rows 20-219, give the corresponding aerosol particle geometric mean diameters (<em>Dp</em>) in unit m, which were used to represent the modelled particle number size distributions (<em>dN/dlogDp</em>). The gas-phase concentrations given in unit ppb are given at the standard temperature and pressure of 273.15 K and 1E5 Pa.</p> <p> </p> <p>The columns in the tables related to the atmospheric relevant runs are classified as follows:</p> <ul> <li><em>1 time</em> [h] ] (simulation time).</li> <li><em>2 DMS</em> [#/cm^3] (Dimethyl sulfide gas phase concentration)</li> <li><em>3 H2SO4</em> [#/cm^3] (Sulfuric acid gas phase concentration)</li> <li><em>4 MSA</em> [#/cm^3] (Methane sulfonic acid gas phase concentration)</li> <li><em>5 HPMTF</em> [#/cm^3] (Hydroperoxymethyl thioformate gas phase concentration)</li> <li><em>6 MSIA</em> [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li><em>7 DMSO</em> [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li><em>8 UVflux</em> [] (Relative UV light intensity, i.e. <em>UVflux </em>= 1 maximum sunlight, <em>UVflux </em>= 0 no sunlight)</li> <li><em>9 sinkCL</em> [#/cm^3/s] (DMS loss rate by reactions with Cl radicals)</li> <li><em>10 sinkOHabs</em> [#/cm^3/s] (DMS loss rate by reactions with OH via the abstraction pathway)</li> <li><em>11 sinkBrO</em> [#/cm^3/s] (DMS loss rate by reactions with BrO radicals)</li> <li><em>12 sinkOHadd</em> [#/cm^3/s] (DMS loss rate by reactions with OH via the addition pathway)</li> <li><em>13 sinkNO3</em> [#/cm^3/s] (DMS loss rate by reactions with NO<sub>3</sub> radicals)</li> <li><em>14 sinkO3aq</em> [#/cm^3/s] (DMS loss rate by reactions with O<sub>3</sub> in the aqueous phase)</li> <li><em>15 PM_CH3SO3</em> [μg/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li><em>16 PM_SO4</em> [μg/m^3] (Sulfate particle mass)</li> <li><em>17 PM_NH4</em> [μg/m^3] (Ammonium particle mass)</li> <li><em>18 PM_NO3</em> [μg/m^3] (Nitrate particle mass)</li> <li>19-218 <em>dN/dlogDp</em> [#/m^3]. (Particle number size distributions)</li> </ul> <p> </p> <p>In this case, the header line, rows 19-218, give the corresponding geometric mean diameters in unit m.</p> <p>The modelled smog chamber experiments result files were named according to the date when the DMS experiments were performed:</p> <p>Exp. DMS1 - 20180405</p> <p>Exp. DMS2 - 20180519</p> <p>Exp. DMS3 - 20180521</p> <p>Exp. DMS4 - 20180523</p> <p>Exp. DMS5 - 20180526</p> <p>Exp. DMS6 - 20190226</p> <p>Exp. DMS7 - 20190301</p> <p> </p> <p><strong>Details for each output table are given here: </strong></p> <p>OutputTable_AtmMain: Base case atmospheric model run</p> <p>OutputTable_lowWindAtm: Atmospheric model run with 2 m/s wind speed</p> <p>OutputTable_PolAtm: Atmospheric model run with higher O<sub>3</sub> and NO<sub>x</sub> concentrations</p> <p>OutputTable_woAqAtm: Atmospheric model simulation without aqueous phase chemistry reactions</p> <p>OutputTable_woCloudAtm: Atmospheric model simulation without clouds</p> <p> </p> <p>OutputTable20180405_1: Base case smog chamber simulation experiment DMS1</p> <p>OutputTable20180519_1: Base case smog chamber simulation experiment DMS2</p> <p>OutputTable20180521_1: Base case smog chamber simulation experiment DMS3</p> <p>OutputTable20180523_1: Base case smog chamber simulation experiment DMS4</p> <p>OutputTable20180526_1: Base case smog chamber simulation experiment DMS5</p> <p>OutputTable20190226_1: Base case smog chamber simulation experiment DMS6</p> <p>OutputTable20190301_1: Base case smog chamber simulation experiment DMS7</p> <p> </p> <p> OutputTable20180519_2: MSIA+OH rate analogues to Yin et al, exp. DMS2</p> <p> OutputTable20180519_3: MSIA+OH rate analogues to Lucas & Prinn et al. , exp. DMS2</p> <p> OutputTable20180519_4: MSIA+OH rate set to 0, exp. DMS2</p> <p> OutputTable20180519_5: No gas partitioning to the liquid water film on the chamber walls, exp. DMS2</p> <p> OutputTable20180519_6: MCM gas-phase chem. setup, exp. DMS2</p> <p> OutputTable20180519_9: CH3SOO isomerization set to 0, exp. DMS2</p> <p> OutputTable20180519_10: HPMTF pathway set to 0, exp. DMS2</p> <p> </p> <p> OutputTable20190226_2: HPMTF pathway analogues to Veres et al. , exp. DMS6</p> <p> OutputTable20190226_3: HPMTF pathway analogues to Yin et al. , exp. DMS6</p> <p> OutputTable20190226_4: HPMTF patway set to 0 , exp. DMS6</p> <p> OutputTable20190226_5: No gas partitioning to the liquid water film on the chamber walls , exp. DMS6</p> <p> OutputTable20190226_6: CH3SOO isomerization set to 0 , exp. DMS6</p> <p> OutputTable20190226_7: MSIA+OH rate set to 0 , exp. DMS6</p> <p> </p> <p> OutputTable20180405_12: O3 wall accommodation coefficient = 1E-8, exp. DMS1</p> <p> OutputTable20180519_12: O3 wall accommodation coefficient = 1E-8, exp. DMS2</p> <p> OutputTable20180521_12: O3 wall accommodation coefficient = 1E-8, exp. DMS3</p> <p> OutputTable20180523_12: O3 wall accommodation coefficient = 1E-8, exp. DMS4</p> <p> OutputTable20180526_12: O3 wall accommodation coefficient = 1E-8, exp. DMS5</p> <p> OutputTable20190226_12: O3 wall accommodation coefficient = 1E-8, exp. DMS6</p> <p> OutputTable20190301_12: O3 wall accommodation coefficient = 1E-8, exp. DMS7</p> <p> </p> <p> OutputTable20180405_13: O3 wall accommodation coefficient = 1E-6, exp. DMS1</p> <p> OutputTable20180519_13: O3 wall accommodation coefficient = 1E-6, exp. DMS2</p> <p> OutputTable20180521_13: O3 wall accommodation coefficient = 1E-6, exp. DMS3</p> <p> OutputTable20180523_13: O3 wall accommodation coefficient = 1E-6, exp. DMS4</p> <p> OutputTable20180526_13: O3 wall accommodation coefficient = 1E-6, exp. DMS5</p> <p> OutputTable20190226_13: O3 wall accommodation coefficient = 1E-6, exp. DMS6</p> <p> OutputTable20190301_13: O3 wall accommodation coefficient = 1E-6, exp. DMS7</p> <p> </p> <p> OutputTable20180405_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS1</p> <p> OutputTable20180519_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS2</p> <p> OutputTable20180521_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS3</p> <p> OutputTable20180523_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS4</p> <p> OutputTable20180526_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS5</p> <p> OutputTable20190226_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS6</p> <p> OutputTable20190301_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS7</p> <p> </p> <p> OutputTable20180405_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS1</p> <p> OutputTable20180519_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS2</p> <p> OutputTable20180521_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS3</p> <p> OutputTable20180523_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS4</p> <p> OutputTable20180526_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS5</p> <p> OutputTable20190226_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS6</p> <p> OutputTable20190301_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS7</p> <p> </p> <p> OutputTable20180405_16: DMS wall accommodation coefficient = 1E-8, exp. DMS1</p> <p> OutputTable20180519_16: DMS wall accommodation coefficient = 1E-8, exp. DMS2</p> <p> OutputTable20180521_16: DMS wall accommodation coefficient = 1E-8, exp. DMS3</p> <p> OutputTable20180523_16: DMS wall accommodation coefficient = 1E-8, exp. DMS4</p> <p> OutputTable20180526_16: DMS wall accommodation coefficient = 1E-8, exp. DMS5</p> <p> OutputTable20190226_16: DMS wall accommodation coefficient = 1E-8, exp. DMS6</p> <p> OutputTable20190301_16: DMS wall accommodation coefficient = 1E-8, exp. DMS7</p> <p> </p> <p> OutputTable20180405_17: DMS wall accommodation coefficient = 1E-6, exp. DMS1</p> <p> OutputTable20180519_17: DMS wall accommodation coefficient = 1E-6, exp. DMS2</p> <p> OutputTable20180521_17: DMS wall accommodation coefficient = 1E-6, exp. DMS3</p> <p> OutputTable20180523_17: DMS wall accommodation coefficient = 1E-6, exp. DMS4</p> <p> OutputTable20180526_17: DMS wall accommodation coefficient = 1E-6, exp. DMS5</p> <p> OutputTable20190226_17: DMS wall accommodation coefficient = 1E-6, exp. DMS6</p> <p> OutputTable20190301_17: DMS wall accommodation coefficient = 1E-6, exp. DMS7</p> <p> </p> <p> OutputTable20180405_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS1</p> <p> OutputTable20180519_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS2</p> <p> OutputTable20180521_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS3</p> <p> OutputTable20180523_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS4</p> <p> OutputTable20180526_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS5</p> <p> OutputTable20190226_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS6</p> <p> OutputTable20190301_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS7</p> <p> </p> <p> OutputTable20180405_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS1</p> <p> OutputTable20180519_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS2</p> <p> OutputTable20180521_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS3</p> <p> OutputTable20180523_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS4</p> <p> OutputTable20180526_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS5</p> <p> OutputTable20190226_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS6</p> <p> OutputTable20190301_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS7</p> <p> </p> <p> OutputTable20180519_20: Liquid water content on walls (LWC wall) = 0.3 mg/m^3, exp. DMS2</p> <p> OutputTable20190226_20: Liquid water content on walls (LWC wall) = 1.5 g/m^3, exp. DMS6</p> <p> OutputTable20190301_20: Liquid water content on walls (LWC wall) = 15 g/m^3, exp. DMS7</p> <p> </p> <p> OutputTable20180519_21: Liquid water content on walls (LWC wall) = 30 mg/m^3, exp. DMS2</p> <p> OutputTable20190226_21: Liquid water content on walls (LWC wall) = 150 g/m^3, exp. DMS6</p> <p> OutputTable20190301_21: Liquid water content on walls (LWC wall) = 1000 g/m^3, exp. DMS7</p> <p> </p> <p><strong>References</strong></p> <p>Wollesen de Jonge, R., Elm, J., Rosati, B., Christiansen, S., Hyttinen, N., Lüdemann, D., Bilde, M., and Roldin, P.: Secondary aerosol formation from dimethyl sulfide – improved mechanistic understanding based on smog chamber experiments and modelling, Atmos. Chem. Phys. <a href="https://doi.org/10.5194/acp-2020-1324">https://doi.org/10.5194/acp-2020-1324</a> (2021) </p> <p>Damian, V., Sandu, A., Damian, M., Potra, F., and Carmichael, G. R.: The kinetic preprocessor KPP-a software environment for solving chemical kinetics, Comput. Chem. Eng., 26, 1567–1579, https://doi.org/10.1016/S0098-1354(02)00128-X, 2002.</p>
Model output from CAABA/MECCA study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)"
<p>This dataset includes the main data obtained during the study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)" (DOI:10.5194/gmd-2023-102). The updated model code can be found at zenodo.org (DOI:10.5281/zenodo.7944174). The data can be used to replicate the results shown in the manuscript. Contained are results produced by the updated CAABA/MECCA (version 4.7.0) and reference data from CAABA/MECCA version 4.5.5. In version 4.7.0, new biogenic and anthropogenic species are introduced to the model (limonene and long-chained alkanes) with refined multiphase chemistry, while new reaction pathways are added for existing compounds (isoprene, benzene and IEPOX). The output is generated to evaluate model results in terms of temperature- and NOx-dependency.</p>
Scripts, models, and data for manuscript "On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media"
<p>This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript</p> <p>Bücker, M., Flores Orozco, A., Undorf, S., and Kemna, A., 2019, <em>On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media</em>, submitted to JGR: Solid Earth.</p> <p>If you find this data useful in your own research, please cite this manuscript.</p>
PLOS Comput. Biol. "Biophysically detailed mathematical models of multiscale cardiac active mechanics": datasets
<p>This repository contains the data accompanying the PLOS Computational Biology paper "<em>Biophysically detailed mathematical models of multiscale cardiac active mechanics</em>", by Francesco Regazzoni, Luca Dedè and Alfio Quarteroni.</p> <p>It contains the following datasets:</p> <ul> <li><strong>steady_state.csv</strong>: steady-state active tension for constant calcium concentration and sarcomere length (Figs. 11, 12, 13 ,14).</li> <li><strong>isometric_twitches.csv</strong>: active tension transients in isometric conditions (Figs. 15, 16, 17).</li> <li><strong>force_velocity_relationship.csv</strong>: force-velocity relationship at different calcium concentrations and sarcomere lenghts (Fig. 18).</li> <li><strong>fast_transient_response.csv</strong>: tension-elongation curve after a fast step in length (Fig. 19).</li> </ul> <p>CSV headers refer to the following variables (and measure units):</p> <ul> <li><strong>Ca</strong> (<em>μM</em>): intracellular calcium concentration.</li> <li><strong>SL</strong> (<em>μm</em>): sarcomere length.</li> <li><strong>active_tension</strong> (<em>kPa</em>): active tension.</li> <li><strong>Delta_L</strong> (<em>nm/hs</em>): step length.</li> <li><strong>velocity</strong> (<em>hs/s</em>): shortening velocity.</li> <li><strong>time</strong> (<em>s</em>): time.</li> </ul>
Computational modelling of metal soap formation in historical oil paintings: the influence of fatty acid concentration and nucleus geometry on the induced chemo-mechanical damage.
<p>Metal soap formation is one of the most wide-spread degradation mechanisms observed in historical oil paintings, affecting works of art from museum collections worldwide. Metal soaps develop from a chemical reaction between metal ions present in the pigments and saturated fatty acids, which are released by the oil binder. The presence of large metal soap crystals inside paint layers or at the paint surface can be detrimental for the visual appearance of artworks. Moreover, metal soaps can possibly trigger mechanical damage, ultimately resulting in flaking of the paint. This paper departs from a recently proposed computational model to predict chemo-mechanical degradation in historical oil paintings, as presented in Eumelen et al. (J Mech Phys Solids 132:103683, 2019). The model describes metal soap formation and growth, which are phenomena that are driven by the diffusion of saturated fatty acids and proceed by a nucleation process from a crystalline nucleus of small size. This results into a chemically-induced strain in the paint, which may promote crack nucleation and propagation. The proposed model is here used to investigate the effects of saturated fatty acid concentration and initial nucleus geometry on the amount of chemo-mechanical damage generated. Numerical simulations show that both factors have a marginal influence on the growth rate of the metal soap crystal, but play a significant role on the extent of fracture induced in the paint.</p>
Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China"
<p>Supplementary data and code to article "Single-Well Microseismic Focal Mechanism Inversions Using Different Source Models: A Case Study in the Ordos Basin, China".</p><p>Transformations among the parameters of the moment tensor model refer to the code package from Tape and Tape (https://github.com/carltape/mtbeach/; https://github.com/carltape/surfacevel2strain; Tape and Tape, 2009, 2012, 2013, 2015).</p><p>Tape, C., P. Muse, M. Simons, D. Dong, and F. Webb (2009). Multiscale estimation of GPS velocity fields, Geophys. J. Int. 179, no.2, 945-971, doi: 10.1111/j.1365-246X.2009.04337.x.</p><p>Tape, W., and C. Tape (2012). A geometric setting for moment tensors, Geophys. J. Int. 190, no. 1, 476–498, doi: 10.1111/j.1365-246X.2012.05491.x.</p><p>Tape, W., and C. Tape (2013). The classical model for moment tensors, Geophys. J. Int. 195, no. 3, 1701–1720, doi: 10.1093/gji/ggt302.</p><p>Tape, W., and C. Tape (2015). A uniform parametrization of moment tensors, Geophys. J. Int. 202, no. 3, 2074–2081, doi: 10.1093/gji/ggv262.</p>
Dataset related to article "Mechanisms of responsiveness to and resistance against trabectedin in murine models of human myxoid liposarcoma"
<p>This record contains data related to article "Mechanisms of responsiveness to and resistance against trabectedin in murine models of human myxoid liposarcoma".</p> <p>Myxoid liposarcoma (MLPS) is a rare soft-tissue sarcoma characterised by the expression of FUS-DDIT3 chimera. Trabectedin has shown significant clinical anti-tumour activity against MLPS. To characterise the molecular mechanism of trabectedin sensitivity and of resistance against it, we integrated genomic and transcriptomic data from treated mice bearing ML017 or ML017/ET, two patient-derived MLPS xenograft models, sensitive to and resistant against trabectedin, respectively. Longitudinal RNA-Seq analysis of ML017 showed that trabectedin acts mainly as a transcriptional regulator: 15 days after the third dose trabectedin modulates the transcription of 4883 genes involved in processes that sustain adipocyte differentiation. No such differences were observed in ML017/ET. Genomic analysis showed that prolonged treatment causes losses in 4p15.2, 4p16.3 and 17q21.3 cytobands leading to acquired-resistance against the drug. The results dissect the complex mechanism of action of trabectedin and provide the basis for novel combinatorial approaches for the treatment of MLPS that could overcome drug-resistance.</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>
Figure 5. A chromosome structure in case we have 2 visible states and 3 invisible states-Neuroevolution Mechanism for Hidden Markov Model
<p>Generating a population of size n of HMMs at random can be performed with some<br> restrictions:<br> - The weights representing the input layer in the chromosome should be always negligible as<br> initial values.<br> - The weights which are involved in summation of 1.0 in the hidden layer part of the<br> chromosome should be exactly 1.0.<br> Let us assume the following case<br> Visible states are 2 and invisible states (observations) are 3, , then we shall have a<br> chromosome as shown in Figure 5.</p>
Figure 6. Two point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
<p>Two point Crossover<br> For the two point crossover we get two parent HMMs and choose at random two cutting points for<br> the weights that have a sum of 1.0 and swap the contents between the crossing points. This is<br> illustrated in the example shown in Figure 6.</p>
Figure 4. A chromosome structure for HMM shown in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>The chromosome which represents the HMM can be extracted from its corresponding neural<br> network. The general structure of the chromosome is divided into two sections, input layer and<br> hidden layer. Each section contains many slots, and each slot represents a weight from one node in<br> that layer to a node in the next layer (from input to hidden and from hidden to output). The number<br> of slots in the input layer is the same number of input nodes in the neural network. In the hidden<br> layer, number of slots is equal to nodes in the output layer multiplied by the nodes in the hidden<br> layer.</p>
Figure 3. Neural network representation for HMM given in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>In our proposed structure, we injected a hidden layer to have a multilayer perceptron which<br> is more efficient than single layer perceptron.<br> To make this process clear, Figure 3 shows the neural networks for the HMM presented in<br> Figure 2.</p>
Figure 9. Adding-Reducing mutation.-Neuroevolution Mechanism for Hidden Markov Model
<p>Adding a small value from one weight and decrement that value to another weight. The chosen<br> weights should be involved in summation of 1.0. Figure 9 shows an example, we add 0.001 from<br> one weight and decrement the same value from another weight.</p>
Figure 1. HMM to describe a relation between the states Med. and High with the observations (invisible states) cold and hot.-Neuroevolution Mechanism for Hidden Markov Model
<p>The advantage of using this technique is that MCPRs are very useful in real time<br> applications and can be adapted over time based on the obtained experience of the networking<br> working process. Again Hewahi[6] proposed a mechanism (algorithm) to evolve and select the best<br> suitable HMM for a given problem using GA, this mechanism lacks to the training process that can<br> be of great usefulness in finding the best HMM.<br> Based on the above mentioned research, the importance of using HMM is increasing<br> rapidly.<br> Let us consider the HMM presented in Figure 1.</p>
Figure 2. HMM with weights and necessary conditions on top of edges-Neuroevolution Mechanism for Hidden Markov Model
<p>Based on the HMM structure in Figure 2, we can perform the following steps:<br> 1. Make the number of nodes of inputs in the input layer of the NN as the number of states<br> (visible states not the observations). Each input node represents one state.<br> 2. Number of nodes in the output layer in the NN is equal to the number of states and<br> observations (visible and invisible states), where each node corresponds to one state (visible<br> or invisible).<br> 3. We construct a hidden layer in NN with n number of nodes, where n is the same number of<br> nodes in the input layer.<br> 4. We make a connection from every input to every hidden layer node with a very negligible<br> weight.<br> 5. Connect every hidden node in the hidden layer to every node in the output layer.<br> 6. Assign weights from the hidden layer to output layer in a way that as every node in the<br> hidden layer corresponding to input state. The weight on top of the link between the hidden<br> node to the output node is the probability value between the states in the HMM.<br> In our proposed structure, we injected a hidden layer to have a multilayer perceptron which<br> is more efficient than single layer perceptron.<br> To make this process clear, Figure 3 shows the neural networks for the HMM presented in<br> Figure 2.</p>
Figure 11. Group mutation-Neuroevolution Mechanism for Hidden Markov Model
<p>This happens by swapping two complete groups with summation of 1.0 with the same criteria.<br> Figure 10 shows a case of this.</p>
Figure 7. One point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
This crossover is performed in the input layer part only. We choose a crossing cut point in the input layer part of the chromosome, and exchange everything before it. This is illustrated in Figure 7.
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>
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