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2,885 results for “transfers”
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning
<p>This is the source code and data for the publication "Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning". Preprint - https://arxiv.org/abs/2112.09182</p>
Music Style Transfer datasets
<p>A collection of MIDI datasets used in my MSc project: Evaluating Music Style Tranfer methods (link TBD).</p>
1H Hyperpolarization of Solutions by Overhauser Dynamic Nuclear Polarization with 13C-1H Polarization Transfer
<p>The dataset here contains the raw data used for the publication 10.1021/acs.jpclett.2c01956</p> <p>There are two folders and one readme file. NMR data in JCAMP or Topspin are in these two folders and are organised according to the figure or table mentioned in the publication. For details, please refer to the readme file. </p>
Trajectories used for Tailoring Charge Transfer Kinetics in Organic Radical Batteries
<p>Trajectories, which are discussed in our work "Tailored Charge Transfer Kinetics in Organic Radical Batteries - A Joint Synthetic-Theoretical Approach"</p> <p>The trajectories were obtained by linearly interpolating in internal coordinates (LIICs) of the relaxed ground state species of molecules A to F, where the charge is either localized on the thiophene backbone (B) or the TEMPO moiety (T1 and T2). Thereby, the program suite pysisyphus (Steinmetzer <em>et. al.</em> 2021) was used to obtain the LIICs. Endpoints represent the fully optimized redox species. The trajectories were used to calculate the intramolecular charge transfer reactions.</p> <p>Trajectories of the following charge transfer reactions are uploaded:</p> <p>1. A<sub>B</sub>→A<sub>T1</sub> (ABtAT1.trj)</p> <p>2. A<sub>B</sub>→A<sub>T2 </sub>(ABtAT2.trj)</p> <p>3. B<sub>B</sub>→B<sub>T1 </sub>(BBtBT1.trj)</p> <p>4. B<sub>B</sub>→B<sub>T2 </sub>(BBtBT2.trj)</p> <p>5. C<sub>B</sub>→C<sub>T1 </sub>(CBtCT1.trj)</p> <p>6. C<sub>B</sub>→C<sub>T2 </sub>(CBtCT2.trj)</p> <p>7. D<sub>B</sub>→D<sub>T1 </sub>(DBtDT1.trj)</p> <p>8. D<sub>B</sub>→D<sub>T2 </sub>(DBtDT2.trj)</p> <p>9. E<sub>B</sub>→E<sub>T </sub>(EBtET.trj)</p> <p>10. F<sub>B</sub>→F<sub>T </sub>(FBtFT.trj)</p>
Data from: Operando Proton Transfer Reaction-Time of Flight-Mass Spectrometry of Carbon Dioxide Reduction Electrocatalysis
<p>Seven top-level folders</p> <p>GC-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of GC-PTR-TOF-MS data</p> <p>LSV-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under linear sweep voltammetry</p> <p>MSCP-PTR-TOF-MS<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS data under multi-step chronopotentiometry</p> <p>PTR-TOF-MS-Calibration<br> - Raw data and Jupyter Notebook used for analysis of PTR-TOF-MS calibration data</p> <p>SEM<br> - Raw images from scanning electron microscope</p> <p>Stability<br> - Raw data of electrochemical stability</p> <p>TEM<br> - Raw images from transmission electron microscopy</p>
Dataset for "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation"
<p>These documents are supplements to the "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation" paper published by the same authors in The Planetary Science Journal in 2022.</p> <p>Are made available:</p> <p>-the Supporting Information document on the performed sensitivity study,<br> "paper_mtWRF_lake_RT_220825_SI.pdf"</p> <p>-the Fortran source code of the radiative transfer module developed for this work,<br> "module_ra_gray.F"</p> <p>-all the netCDF simulation outputs and a list describing their parameters,<br> "run-##.nc.gz"<br> "list_simulations_2D_paper2022_RT_zenodo.pdf"</p> <p>-the Python codes to plot figures from the netCDF output files,<br> "mtwrf_analysis_#D_#.py"</p>
Excitation energy transfer and vibronic coherence in intact phycobilisomes — multidimensional electronic spectroscopy data set and MATLAB and Julia analysis code
<p>Data sets used in the article "Excitation energy transfer and vibronic coherence in intact phycobilisomes" by Sil et al. The phycobilisomes were isolated from the short-filament mutant (SF33) of <em>Fremyella diplosiphon</em> UTEX 481 (also known as <em>Tolypothrix</em> sp. PCC 7601). Multidimensional electronic spectroscopy was performed with 6.7 fs mid-visible pulses (520–700 nm) using a pump–probe optical configuration using adaptive pulse shaping techniques. In addition to the full set of two-dimensional spectra and analysis files generated using global and target modeling and analysis of coherences (3DES oscillation maps), we provide here a linear absorption spectrum with phycobiliprotein component analysis as well as a set of 2D excitation–emission fluorescence spectra of intact and broken phycobilisome preparations. </p> <p>Sil, S.; Tilluck, R. W.; Mohan TM, N.; Leslie, C. H.; Rose, J. B.; Domínguez-Martín, M. A.; Lou, W.; Kerfeld, C. A.; Beck, W. F. Excitation energy transfer and vibronic coherence in intact phycobilisomes. Nat. Chem. (2022), DOI: 10.1038/s41557-022-01026-8.</p> <p><a href="https://urldefense.com/v3/__https://www.nature.com/articles/s41557-022-01026-8__;!!HXCxUKc!yaVwTZFk8T-j3ROhygpOGW5Xy_E2wQvf-QgNGr9FZZbp4oNpfp_ZmhkdWYLdg2mKSDP8yYrNAZs$">https://www.nature.com/articles/s41557-022-01026-8</a></p> <p> </p> <p> </p>
Unidirectional transmembrane photoelectron transfer with artificial metallopeptides
<p>Unidirectional transmembrane electron transfer is an essential process in natural photosynthesis, where it plays a key role in separating reaction products and slowing charge recombination.<sup>1</sup> Making artificial molecules that functionally mimic photosynthetic proteins and achieve transmembrane electron transfer is already a chemical endeavour.<sup>2–7</sup> Distinguishing genuine transmembrane electron transfer through an impermeable lipid membrane, from light-induced leakage of electron donors through the membrane followed by photoelectron transfer on one side of the bilayer, is even more difficult.<sup>8–10</sup>Here, we report two artificial metallopeptides, <strong>WALP23-Ru<sub>2</sub></strong> and <strong>WALP23-Re<sub>2</sub></strong>, that drive photoelectron transfer from an electron donor located inside a liposome, to an acceptor located outside. A sensitive membrane leakage assay was used to demonstrate that <strong>WALP23-Re<sub>2</sub></strong> indeed achieved genuine photoelectron transfer through the membrane, with negligible contribution from leakage. By contrast, photoelectron transfer with <strong>WALP23-Ru<sub>2</sub></strong> was clearly the result of leakage of the electron donor through the membrane, followed by photoinduced electron transfer on one side of the membrane. These results demonstrate the unique potential of neutral metallopeptides such as <strong>WALP23-Re<sub>2</sub></strong> to drive transmembrane electron transfer in artificial photosynthetic systems, while highlighting the importance of leakage studies for the understanding of how such systems really work.</p>
Subset of 300 out of 3000 Prepared Sentinel 2 Scenes for Transfer Learning and Super-sampling.
<p>This dataset contains a random subset of 300 out of 3000 Sentinel 2 scenes prepared for Transfer Learning and Super-sampling. It comes in the form of zipped NumPy arrays in the npz format. The files have are named in the following fashion:</p> <p>latutide+latitude_decimals_longitude+longitude_decimals_month_of_the_year_for_mosaic. </p> <p>Each file contains:</p> <p>bands.npy: The Sentinel 2 bands in 10m resolution uint10: B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12. The 20m bands have been resampled using bilinear resampling.</p> <p>nir.npy: B08 Resampled to 20m using average resampling and then resampled to 10m using bilinear. Useful for training super-sampling models.</p> <p>scl.npy: The Sentinel 2 Scene Classification file. Contains information on cloud cover and land cover.</p> <p>sincos.npy: Contains the latitude, longitude, and time of capture for each pixel encoded to sine and cosine waves in the [0,1] interval. The is useful when training a model to predict where on the globe an image was captured.</p> <p>The images can be processed to patches using the buteo toolbox: </p> <p>`pip install buteo --upgrade</p> <p>`import buteo as beo`</p> <p>`beo.get_patches(beo.raster_to_array("path_to_bands"))`</p> <p> </p>
Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors
<p>The "Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors" can be used to test machine learning approaches on their capability of interpreting sensor patterns of commercially available MOS gas sensors, i.e., SGP40 (Sensirion AG, Stäfa, Switzerland), to predict multiple different gas concentrations and the relative humidity. Furthermore, the dataset can be used to test the transferability between sensors. <br> The dataset was recorded with the help of a custom-built gas mixing apparatus (GMA). The GMA allows applying well-known gas mixtures to multiple gas sensors. For this experiment, three SGP40 with four sub-sensors each were exposed to 900 different unique gas mixtures (UGMs) consisting of ten different gases. In detail, the dataset consists of eight volatile organic compounds (VOCs) (acetic acid, acetone, ethanol, ethyl acetate, formaldehyde, isopropanol, toluene, and xylene), two background gases (carbon monoxide and hydrogen), and the relative humidity at 20 °C. During exposure, the sensors are operated in a temperature-cycled operation. The temperature cycle consists of alternating high and low-temperature phases. The high-temperature phases are set at 400 °C and have a duration of 5 seconds, while the low-temperature steps increase in 25 °C steps from 100 °C-375 °C, where each step has a duration of 7 seconds. The only exception is sub-sensor 4, where the temperature is only alternated between 250 °C and 300 °C. The total duration of the temperature cycle is 144 seconds, and during this time, the logarithmic sensor resistance is read out at 10 Hz. Each gas mixture was recorded for ten temperature cycles to ensure that stable gas mixtures were applied to the sensor. Only stable samples 6 (not always),7,8, and 9 were used for further evaluation. The 900 UGMs can be separated into three parts, and for each part, the mixtures were generated based on Latin hypercube sampling and the ranges specified in Table 1.</p> <table> <caption>Tabel 1: Uniform distributed ranges for all gasses within the gas mixtures</caption> <tbody> <tr> <td> </td> <td>UGM 1-200</td> <td>UGM 201-500</td> <td>UGM501-900</td> </tr> <tr> <td>Carbon monoxide</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> </tr> <tr> <td>Hydrogen</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> </tr> <tr> <td>Relative humidity</td> <td>25 - 80 %</td> <td>25 - 80 %</td> <td>25 - 80 %</td> </tr> <tr> <td>Acetic acid</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Acetone</td> <td>3 - 50 ppb</td> <td>3 - 150 ppb</td> <td>3 - 500 ppb</td> </tr> <tr> <td>Ethanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Ethyl acetate</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Formaldehyde</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 300 ppb</td> </tr> <tr> <td>Isopropanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Toluene</td> <td>1 - 75 ppb</td> <td>1 - 75 ppb</td> <td>1 - 250 ppb</td> </tr> <tr> <td>Xylene</td> <td>2 - 150 ppb</td> <td>2 - 150 ppb</td> <td>2 - 500 ppb</td> </tr> </tbody> </table> <p>To be able to use this dataset for transfer learning, the dataset consists of three different SPG40; two are from the same batch (sensor A and sensor B), and sensor C is from a different batch. <br> The dataset consists of the sensors' data and a target for evaluation. The data is already split into training and Validation and is stored in cells for each sensor: <br> sensorA_train<br> sensorA_test<br> sensorB_train<br> sensorB_test<br> sensorC_train<br> sensorC_test</p> <p> Each sensor cell contains four arrays, one for each sub-sensor within one SGP40. The number of rows in the arrays represents the number of observations (693 for test and 2401 for training), and the number of columns represents the number of samples per observation (1440).<br> The targets, i.e., the concentrations of each gas, are given in the target_train and targe_test structs. Since the data were recorded simultaneously, those structs can be used as targets for all sensors. The ten different gases, relative humidity, and TVOCsens are actual targets, while the range parameter represents the specific unique gas mixture ID.</p> <p>Although this is a mat file, it can be opened as an hdf5 file.</p>
Data for Novel Approach to Quantify Sediment Transfer and Storage in Rivers with Feldspar Single-Grain pIRIR
<p>Raw data of feldspar post infra-red infra-red (pIRIR) stimulated luminescence signal from modern sand from New Zealand rivers (Rakaia, six samples; Waimakariri, height samples) used in the paper Guyez et al. (2022) submitted to JGR: Earth Surface. The samples were collected in February 2020 by S. Bonnet and A. Guyez. They were measured in the Netherlands Center for Luminescence dating (Wageningen University and Research). </p>
Level 3 data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from solutions of a nested configuration off the coast of California. These data are a subset of the solutions reported by Romero et al. 2020.</p> <p>These data are Level 3 of the nested configuration with a horizontal resolution of 120 m.</p> <p>Data are in Netcdf format including the metadata.</p> <p>The simulation period December 2006</p> <p>List of files:</p> <p>L2_Dec2006.nc -- December control solution only forced by winds</p> <p>L2_cew_Dec2006.nc -- December solution including forcing by both winds and currents</p> <p>Grid files can be found at: https://doi.org/10.5281/zenodo.7140916</p>
Level 2 Spring data in support of Modulation of Bubble Mediated Gas Transfer due to Wave-Current Interactions
<p>WaveWatchIII data output from solutions of a nested configuration off the coast of California. These data are a subset of the solutions reported by Romero et al. 2020.</p> <p>These data are Level 2 of the nested configuration with a horizontal resolution of 270 m.</p> <p>Data are in Netcdf format including the metadata.</p> <p>The two simulation periods are December 2006 and Spring 2007.</p> <p>List of files:</p> <p>L2_Mar2007.nc -- Spring control solution only forced by winds</p> <p>L2_cew_Mar2007.nc --- Spring solution including forcing by both winds and currents.</p> <p>Grid files can be found at: https://doi.org/10.5281/zenodo.7140916</p> <p> </p> <p> </p> <p> </p>
Raw data accompanying the manuscript "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse"
<p>These are the raw datasets used to generate the figures for the manuscript entitled "Cost-effective high-speed, three-dimensional live-cell imaging of HIV-1 transfer at the T cell virological synapse". The data files are 3D image stacks of a custom-built wide field deconvolution fluorescence microscope (.tif) and super-resolution structured illumination microscopy data (.dv) of Jurkat T cells transferring HIV-1 virus particles to previously uninfected primary T cells.</p>
Linked collectors and determiners for: Two new genera of Australian dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) with the description of six new species and transfer of six described species.
Natural history specimen data linked to collectors and determiners held within, "Two new genera of Australian dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) with the description of six new species and transfer of six described species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/43ebda6c-201a-46af-8b54-e7980b8d79e1">https://bionomia.net/dataset/43ebda6c-201a-46af-8b54-e7980b8d79e1</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/43ebda6c-201a-46af-8b54-e7980b8d79e1">https://gbif.org/dataset/43ebda6c-201a-46af-8b54-e7980b8d79e1</a>. Formatted as a Frictionless Data package.
FIGURE 3 in Freshwater fish richness baseline from the São Francisco Interbasin Water Transfer Project in the Brazilian Semiarid
FIGURE 3 | Freshwater fish species from the São Francisco Interbasin Water Transfer Project basins in the Brazilian semiarid. A = Hemigrammus brevis, endemic species of São Francisco Ecoregion (SFRE); B = Moenkhausia costae and C = Psellogrammus kennedyi, shared species between SFRE and Mid-Northeastern Caatinga Ecoregion (MNCE); D = Aspidoras menezesi, endemic species of Jaguaribe basin (JAG); E = Hypostomus sertanejo, endemic species of MNCE; F = Parotocinclus spilurus, endemic and endangered species of JAG; G = Tatia bockmanni, endemic species of SFRE; H = Cichlasoma orientale, shared species from all basins of SFR-IWT; I = Geophagus brasiliensis, shared species between SFRE and MNCE; J = Colossoma macropomum, non-native species shared between SFRE and Piranhas-Açu basin; K = Parachromis managuensis, non-native species shared between SFRE and Paraíba do Norte basin; L = Xiphophorus helleri, non-native species of JAG.
FIGURE 2 in Freshwater fish richness baseline from the São Francisco Interbasin Water Transfer Project in the Brazilian Semiarid
FIGURE 2 | Sampling sites in the São Francisco Interbasin Water Transfer Project basins in the Brazilian semiarid. A = Canals under construction near the rio São Francisco main channel, and B = near Sertânia, Pernambuco State (PE), C = Rio Pajeú, tributary of the São Francisco basin, PE, D = Rio São Francisco near Petrolina, PE, E = Temporary pool in rio Jaguaribe basin in Russas, Ceará State (CE), F = Rio Jaguaribe in Crato, CE, G = Rio Apodi-Mossoró in Pau dos Ferros, Rio Grande do Norte State (RN), H = Rio Apodi-Mossoró in Pau dos Ferros, RN, I = Rio Seridó, tributary of the Piranhas-Açu basin in Caicó, RN, J = Rio Piranhas-Açu, Jardim de Piranhas, RN, K = Rio Paraíba do Norte in Barra de Santana, Paraíba State (PB), L = Rio Paraíba do Norte in São João do Cariri, PB.
FIGURE 1 in Freshwater fish richness baseline from the São Francisco Interbasin Water Transfer Project in the Brazilian Semiarid
FIGURE 1 | Sampling sites of the freshwater fish species in the São Francisco Interbasin Water Transfer Project basins in the Brazilian semiarid.
Infrared Radiative Transfer Calculations Dataset
<p>This dataset presents the results of a series of experiments conducted varying combinations of two versions of the HITRAN database (2016 and 2020) and two versions of the MT_CKD water vapor continuum model (3.2 and 4.1.1). Five atmospheric models representing different climatic conditions (Tropical, Mid Latitude Winter, Mid Latitude Summer, Sub Arctic Winter, and Sub Arctic Summer) are employed. Key atmospheric gases, including CO2, O3, CH4, CO, N2O, and O2, are prescribed at each atmospheric model. The dataset includes calculations with all gases present as well as experiments removing individual gases (specifically CO2, O3, and water vapor). Outputs consist of upwelling and downwelling infrared radiation and cooling rate profiles at 44 atmospheric heights from the surface to 95 km. Data are provided at 0.1 cm^-1 resolution across the spectral range from 10 to 3000 cm^-1. For each experiment, a file in “.txt” format is available at each atmospheric height containing the flux each wavenumber. This dataset offers valuable insights into the impact of different atmospheric compositions and models on radiative transfer processes and cooling rates, contributing to a better understanding of Earth's climate system.</p> <p>Similar experiments are compacted in .tar files:</p> <p>var_profile_HITRAN<em>_lev_</em>lllll_mtckd{<em>_wo</em>flag}<em>.tar</em></p> <table> <tbody> <tr> <td>var (3 digits)</td> <td>profile (3 digits)</td> <td>HITRAN (4 digits)</td> <td>lllll (5 digits)</td> <td>mtckd (3 digits)</td> <td>flag (3 digits)</td> </tr> <tr> <td>"olr": upward radiation flux</td> <td>"tro": Tropical</td> <td>2016</td> <td>Output height in meters. 5 digits. Ranging from the surface (i.e. "00000") to 95 km (i.e. "95000")</td> <td>"3.2": v3.2</td> <td>"H2O": experiment removing water vapor.</td> </tr> <tr> <td>"dlr": downward radiation flux</td> <td>"mls": Mid Latitude Summer</td> <td>2020</td> <td> </td> <td>"4.1": v4.1.1</td> <td>"CO2": experiment removing carbon dioxide.</td> </tr> <tr> <td>"coo": cooling rates</td> <td>"mlw": Mid Latitude Winter</td> <td> </td> <td> </td> <td> </td> <td>"O3": experiment removing ozone.</td> </tr> <tr> <td> </td> <td>"sas": Sub Artic Summer</td> <td> </td> <td> </td> <td> </td> <td>"BFX": experiment without the "B linear to tau" aproximation.</td> </tr> <tr> <td> </td> <td>"saw": Sub Artic Winter</td> <td> </td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table> <p>To untar the file:</p> <pre><code>tar -xzf var_profile_HITRAN_lev_lllll_mtckd_woflag.tar</code></pre> <p>Each tar file consists of 880 text files.</p>
Data & Codes used in: Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)
<p>Here we provide the data, codes, and outline the procedure to reproduce the results presented in the paper "Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)" by E. Ventura-Macias, P. M. Martinez, Rubén Pérez, and J. G. Vilhena.</p> <p>The following sections contain a detailed description of the data and codes. At the end of this README, you will find instructions on how to generate the classical force-field parameters (Morse and Lennard-Jones) from the potential energy surfaces (PES) computed at the DFT level.</p> <p>The procedure is general and applies to any given pair of molecule and surface. The provided codes will allow you to swiftly generate the PES at the DFT level, fit the Lennard-Jones and Morse potentials, and test them in a LAMMPS MD simulation.</p> <p>For a thorough explanation of the procedure and the relevance of these results, please refer to the original publication (ARTICLE_DOI).</p> <p>If you find this helpful, please consider citing the article (ARTICLE_DOI).</p> <h2>Data structure</h2> <p>The data is organized in the following way:</p> <ol> <li> <p>DFT</p> <ul> <li>The equilibrium adsorption geometry of methanethiol (MTH), propanethiol (PTH), and cysteine (CYS) for the Au-mol configuration with PBE+DFT-D3.</li> <li>Potential Energy Surface (PES) computed at the DFT level (Figure 3 of the main manuscript).</li> <li>The scripts used to generate the PES.</li> </ul> </li> <li> <p>MD</p> <ul> <li>Fitting code and general instructions on how to use it.</li> <li>General input scripts used to generate MD data within LAMMPS.</li> </ul> </li> </ol> <h3>DFT Data</h3> <p>The DFT data in <code>DFT.zip</code> is organized in the following way:</p> <ul> <li> <p><code>DFT_PES/</code></p> <p>This folder contains the DFT potential energy surfaces (PES) for the interaction of the sulfur atom of methanethiolate (<code>mth</code>), propanethiolate (<code>pth</code>), and cysteine (<code>cys</code>) with the Au(111) surface and the necessary scripts to reproduce it.</p> <ul> <li> <p><code>results/</code></p> <p>The PES are given in one csv file per molecule named as <em><code>mol</code></em> + <code>_PES_S-Au111.csv</code>, where <em><code>mol</code></em> is the molecule name. Columns are as follows:</p> <table> <tbody> <tr> <td><strong>Label</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><em>i</em></td> <td>calculation number</td> </tr> <tr> <td><em>site</em></td> <td>adsorption site</td> </tr> <tr> <td><em>z</em></td> <td>distance of the S atom to the surface</td> </tr> <tr> <td><em>deltaz</em></td> <td>distance difference from the minimum energy position of the S atom</td> </tr> <tr> <td><em>pbed3</em></td> <td>PBE+D3 binding energy</td> </tr> <tr> <td><em>pbe</em></td> <td>PBE component of the binding energy</td> </tr> <tr> <td><em>d3</em></td> <td>DFT-D3 component of the binding energy</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li> <ul> <li> <p><code>mth/</code> | <code>pth/</code> | <code>cys/</code></p> <p>Each folder contains the CONTCAR (VASP) file for the optimized geometry of the molecule adsorbed on the Au(111) surface with PBE+D3.</p> </li> <li> <p><code>setup_grid.py</code></p> <p>Python script to set up the POSCAR files for the PES calculations.</p> </li> <li> <p><code>read_results.py</code></p> <p>Python script to read the results of the PES calculations.</p> </li> <li> <p><code>sub_array.sh</code></p> <p>Bash script to submit the PES calculations to an SLURM-based cluster.</p> </li> <li> <p><code>INCAR</code> | <code>KPOINTS</code> | <code>surf.CONTCAR</code></p> <p>VASP input files for the PES calculations.</p> </li> </ul> </li> </ul> <h3>MD fitting</h3> <p>The MD data in MD.zip is are organized as:</p> <ul> <li> <p><code>Fitting/</code></p> <ul> <li><code>optimize_Morse.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of Morse potential.</p> <ul> <li><code>optimize_LJ.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of LJ potential.</p> </li> <li> <p><code>Histogram/</code></p> <ul> <li><code>in.test</code></li> </ul> <p>LAMMPS input script to extract an XY file for the position of the S atom in an NVT simulation.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>sheng.eam</code></li> </ul> <p>EAM potential file in case Au dynamics are wished to be included.</p> </li> <li> <p><code>Single_point_scan/</code></p> <ul> <li><code>in.scan</code></li> </ul> <p>LAMMPS input script to perform single-point energy scan of a given molecule.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>launch.sh</code></li> </ul> <p>Launches the single point scan.</p> <ul> <li><code>plot_scan.py</code></li> </ul> </li> <li> <p><code>Molecules</code></p> <p>LAMMPS sample geometries for the 3 molecules.</p> </li> </ul> <h2>Steps to reproduce the results</h2> <p>The PES calculations were performed using the VASP code and two Python scripts for setup and results parsing.</p> <h3>Requirements</h3> <ul> <li>VASP (tested with version 5.4.4) <ul> <li><code>PAW_PBE</code> pseudopotentials set version 5.4</li> </ul> </li> <li>Python3 with packages: <ul> <li>ASE (Atomic Simulation Environment)</li> <li>Numpy</li> <li>Pandas</li> <li>matplotlib (optional)</li> </ul> </li> </ul> <h3>Steps</h3> <p>Each molecule has its own directory with the necessary files to reproduce the results. The following steps are for the methanethiolate molecule (<code>mth</code>).</p> <ol> <li>Set up the grid of points for the PES calculations by running the <code>setup_grid.py</code> script. It will create a subfolder <code>run/</code> inside the molecule's directory with the POSCAR files for each point in the grid.</li> </ol> <blockquote> <p>python setup_grid.py mth</p> </blockquote> <ol> <li> <p>Create the corresponding <code>POTCAR</code> file and place it in the molecule's directory.</p> </li> <li> <p>Change the <code>sub_array.sh</code> script to match the number of calculations in the array numbers and the MOL variable.</p> </li> </ol> <pre><code>#SBATCH --array=1-number of calculations MOL=mth</code></pre> <ol> <li>Submit the calculations to a SLURM-based cluster by running the <code>sub_array.sh</code> script from the <code>DFT_PES</code> directory.</li> </ol> <blockquote> <p>sbatch sub_array.sh</p> </blockquote> <ol> <li>After the calculations are finished, run the <code>read_results.py</code> script to parse the results and generate the PES csv files. It takes the arguments <code>--surf</code> and <code>--mol</code> to specify the surface and molecule PBE+D3 and D3 total energies.</li> </ol> <blockquote> <p>python read_results.py mth --surf -128.5228 -17.7147 --mol -22.6026 -0.0083</p> </blockquote> <h3>Reference values for the PBE+D3 and D3 surface and molecule energies</h3> <table> <tbody> <tr> <td> </td> <td><strong>Surface</strong></td> <td> </td> <td><strong>Molecule</strong></td> <td> </td> </tr> <tr> <td><strong>Molecule</strong></td> <td><strong>PBE+D3<br></strong></td> <td><strong>D3</strong></td> <td><strong>PBE+D3</strong></td> <td><strong>D3</strong></td> </tr> <tr> <td>mth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-22.6026</td> <td>-0.0083</td> </tr> <tr> <td>pth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-55.8615</td> <td>-0.0086</td> </tr> <tr> <td>cys</td> <td>-128.5228</td> <td>-17.7147</td> <td>-74.0338</td> <td>-0.1611</td> </tr> </tbody> </table> <h3>Fitting procedure</h3> <p>Both the .xyz and .csv files should be located at the same folder as the script. Then, simply run the code (<code>mth</code> is used as an example):</p> <blockquote> <p>python optimize_Morse.py mth</p> </blockquote> <p>or</p> <blockquote> <p>python optimize_LJ.py mth</p> </blockquote> <p>The script will print the optimized parameters: [De, re, α] or [ϵ, σ] for Morse or LJ respectively. It will also plot a fitting plot and a birdview of the resulting PES.</p> <h3>Using the potential. Histogram example</h3> <p>The code will run for the optimized <code>mth</code> Morse parameters and extract a <code>occ.lammpstrj</code> containing the (x,y) positions of the S atom throughout the NVT simulation. Note that only Au-S interaction is included. Start as:</p> <blockquote> <p>lmp -in in.test</p> </blockquote> <p>This is easily adaptable to other routines or molecules and is thought to be a generic LAMMPS starting input.</p> <h3>Single-point energy scan</h3> <p>Go to the 2C) folder and launch the scan with:</p> <blockquote> <p>./launch.sh</p> </blockquote> <p>This will create a folder named <code>fine_scan</code> containing 128 folders. Each folder is assigned to an (x,y) position. Then, inside each folder, a single-point energy evaluation is performed at various Z heights around the absolute minima.</p> <p>The <code>in.scan</code> file should be modified accordingly with the appropriate potentials. It is set to perform the scan with the optimized Morse potential by default.</p> <p>The output is gathered in the <code>E_readout.dat</code> folder with the following structure (all energies in eV):</p> <table> <tbody> <tr> <th>Total Energy</th> <th>Intramolecular energy</th> <th>Au-mol vdW interaction energy</th> <th>Au-S interaction energy</th> </tr> </tbody> <tbody> <tr> <td>-133.62</td> <td>0.123112</td> <td>-0.2403</td> <td>-1.31765</td> </tr> <tr> <td>-133.721</td> <td>0.123112</td> <td>-0.28-401</td> <td>-1.37853</td> </tr> </tbody> </table> <p>Therefore, the total adsorption energy will be the sum of the last two columns.</p> <p>Energies are ordered in increasing Z for the same (x,y) point. That is, the first 12 lines correspond to 12 heights of the starting (x,y) coordinate, the next 12 lines to heights at the second (x,y) configuration and so on.</p> <p>The python script <code>plot_scan.py</code> may be used to plot the results. The <code>E_readout.dat</code> file and <code>.csv</code> must be in the same folder.</p> <blockquote> <p>python plot_scan.py mth</p> </blockquote>
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