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"en": "The dataset includes understory reflectance spectra in hyperspectral resolution (350–2300 nm), fractional cover of understory, and forest inventory variables from different types of boreal forest stands. The measurements were made in situ in 36 forest stands in Hyytiälä, Finland (61°50’N, 24°17’E) during 2018 and 2019 peak growing seasons (day of year 165–190) under diffuse illumination conditions. Each study stand represents a forest site (fertility) type: OMT (herb-rich), MT (mesic), VT (sub-xeric), or CT (xeric), based on Cajander’s theory of forest types [1]. The fractional cover of understory was estimated for each stand from nadir view photographs of vegetation quadrats using the following classes: green vegetation (%-green), plant litter (%-litter), and lichen (%-lichen). Spectral data of the understory were measured in nadir view using ASD FieldSpec4 spectrometer in tandem with bare fiber sensor optics and a pistol grip. The spectral data are hemispherical-conical reflectance factors (HCRF), calculated from the ratios of measurement signals from the understory and white reference panel, with dark currents readily subtracted. A detailed description of the methodology is provided by Forsström et al. [2].\r\n\r\nMeasurement data from altogether 36 study stands are organized in spread sheets as follows: the first spread sheet includes the description of the variables, the second spread sheet include both the forest inventory data and fractional cover data, and the rest of the spread sheets (numbered 1-36) include the spectral data. Spectral data are further organized in columns, so that the first column is a list of measured wavelengths and columns 2-16 include the corresponding HCRFs from 15 sampling positions (~0.8 m apart along a transect line) per study stand. HCRFs in wavelength regions of high noise are assigned with NaN values for clarity. \r\n\r\nWe thank Ville Ranta and Daniel Schraik for collaboration. Data collection and preparation was supported by the Academy of Finland [BOREALITY, grant number 286390; and DIMEBO, grant number 3323004]; and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme [grant agreement No 771049]. The article reflects only the authors’ view and the Agency is not responsible for any use that may be made of the information it contains.\r\n\r\nIf you use this dataset, please cite Forsström et al. [2,3].\r\n\r\n[1] Cajander, A.K., 1926. The theory of forest types. Acta Forestalia Fennica, 29(3), article id 7193. doi:10.14214/aff.7193.\r\n[2] Forsström, P.R., Juola, J., Rautiainen, M. 2021. Relationships between understory spectra and fractional cover in northern European boreal forests. Agricultural and Forest Meteorology. doi:10.1016/j.agrformet.2021.108604\r\n[3] Forsström, P.R., Juola, J., Hovi, A., Rautiainen, M., 2021. Dataset of understory reflectance spectra and fractional cover in a boreal forest area in Finland. Mendeley Data, V1, doi:10.17632/2g9nkcdj53.1"
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"en": "The computational hydrogen evolution activity of Pt(111) remains controversial due to apparent discrepancies with experiments concerning rate-determining activation free energies and equilibrium hydrogen coverages. A fundamental source of error may lie within the static representations of the metal–water interface commonly employed in density functional theory (DFT)-based kinetic models neglecting important entropic effects on reaction dynamics. In this work, we present a dynamic reassessment of the Volmer–Tafel hydrogen evolution pathway on Pt(111) through DFT-based constrained molecular dynamics simulations and thermodynamic integration. Hydrogen coverage effects are gauged at two distinct surface saturations, while the critical potential dependence and constant potential conditions are accounted for using a capacitive model of the electrified interface. The uncertainty in the highly nontrivial treatment of the electrode potential is carefully examined, and we provide a quantitative estimation of the error associated with dynamically simulated electrochemical barriers. The dynamic description of the electrochemical interface promotes a substantial decrease of the Tafel free energy barrier as the coverage is increased to a full monolayer. This follows from a decreased entropic barrier due to suppressed adlayer dynamics compared to the unsaturated surface, a detail easily missed by static calculations predicting notably higher barriers at the same coverage. Due to observed endergonic adsorption of active hydrogen intermediates, the Tafel step remains rate-determining irrespective of the coverage as illustrated by composed Volmer–Tafel free energy landscapes. Importantly, our explicitly dynamic approach avoids the ambiguous choice of frozen solvent configuration, decreasing the reliance on error cancellation and paving the way for less biased electrochemical simulations."
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"en": "This database contains all the necessary data (and metadata) for DFT calculations, PP-AFM and PP-STM simulations presented in:\r\n\r\nQitang Fan, Linghao Yan, Matthias W. Tripp, Ondřej Krejčí, Stavrina Dimosthenous, Stefan R. Kachel, Mengyi Chen, Adam S. Foster, Ulrich Koert, Peter Liljeroth and J. Michael Gottfried :\r\n\r\nBiphenylene network: A nonbenzenoid carbon allotrope ,\r\n\r\nScience 372, pp. 852-856 (2021); DOI: 10.1126/science.abg4509\r\n\r\n \r\n\r\nAltogether 12 different geometries of Biphenylene ribbons - 6 different widths and types of 2 edges (H and HF) - were calculated.\r\n\r\n \r\n\r\nAll the DFT calculations, were obtained with FHI-AIMS[1], HSE06[2] & TS-vdW[3] and light basis set (see `control.in` for more details)\r\n\r\n \r\n\r\nThe optimisations were done via FHI-AIMS (v. aims.191119.mpi.scalapack.x ). The inputs (and optimised geometries) can be found in zipped folder `opt` with separated folder for each type of ribbon. The parameters for the calculations can be found in `control.in` file. Input geometry in `geometry.in` and the optimized geometry in `geometry-opt.in`. The `run_aims_scalapack.slrm` was used for starting the calculations.\r\n\r\n \r\n\r\nThe calculations of bands (and plotted pDOS). The inputs, scripts and outputs can be found in zipped foled `bands` The FHI-aims version for these calculations was: aims.200229.mpi.scalapack.x, except for Biphenyl ribbon 6 ( aims.191119.mpi.scalapack.x ) and Biphenyl ribbon 21 ( aims.200422.mpi.scalapack.x ). The FHI-aims calculations were performed using standard inputs — `control.in` with physical settings and geometry.in with the geometries. For plotting the band structure the same way as it was done in the publication, the edges of the valence bands and conduction bands were extracted by means of the `grep_from_output.sh` scripts (except for 21H and 21HF, which are metallic) that produced `for_shift.txt` file. The important data in each directory — `atom_proj_dos_*****_raw.dat` and `band1001.out` together with the energy shifts — were then processed and plotted with Wolfram Mathematica (11.3.0.0) notebook `GNR_DOS_plot_and_view.nb` (optionally see the pdf). The plots for each width of the ribbons are in`bandsNdos_thor***.png`. \r\n\r\n \r\n\r\nThe PP-AFM[4] and PP-STM[5] calculations simulating CO-tip AFM and STM image are described in zipped folder `PP_simulations`; The calculations started with creation of 9x1x1 cell, because the PP-STM simulations can be done only at the Gamma k-point at the time. At the same time. the z-vector of the cell is lowered to 30 Å in order to save the memory space on the PP-AFM calculations. All of this was done via ASE[6] (by hand, not shown here). The (processed) geometries are written in `geometry.in`. The FHI-aims computations performed with aims.200422.scalapack.mpi.x. (with options saved in `control.in`) produced `cube_001_hartree_potential.cube`, containing the Hartree potential, and `KS_eigenvectors.band_1.kpt_1.out`, with the Eigen-energies and Eigen-vectors for the STM simulations. Both of these files are not saved (due to space reasons).\r\n\r\n \r\n\r\nThe PP-AFM simulations continued using the standard CO settings stored in `params.ini` using the `run_PPAFM.sh` script. The PP-AFM version was the Master branch:\r\n\r\n \r\n\r\n>>> Krejci Ondrej committed on 25 Mar 2020 \r\n\r\n>>> 1 parent 805f0bd\r\n\r\n>>> commit 76f6104d52b9ce44b6bbf76ed96846bcc5433c96\r\n\r\n \r\n\r\nThe resulting images are `df_atoms_002.png`. For plotting the overlaid atoms, it is important to have `input_plot.xyz` file with geometry (in the same directory), that will be plotted. This file was done from the 1x1x1 cell geometry through ASE.\r\n\r\n \r\n\r\nThe PP-STM simulations (dI/dV simulations) were done with the (last) python2 master version:\r\n\r\n \r\n\r\n>>> ondrejkrejci committed on 25 May 2020\r\n\r\n>>> 1 parent b66fa0d\r\n\r\n>>> commit 2b088e274240fd756bc8c2f53f870fe7a2e833ac\r\n\r\n \r\n\r\nThe original PPSTM_simple.py script was changed to the 2 new scripts used for the STM simulations: `PPSTM_cb.py` (conduction band) and `PPSTM_vb.py` (valence band). The Fermi Level was changed to the centre of the gap, with energies obtained of from the `for_shift.txt` created by the `grep_from_output.sh` script. The exceptions are 21H and 21HF, where the valence band was computed (just because of a history reason) through two separate runs with `PPSTM_e0.2_s.py` and `PPSTM_e0.2_pxy.py` and the final image was then created with `SUM_e0.2.py`. In these two cases, the Fermi Level was not changed, since these ribbons are metallic. \r\n\r\nThe 2 outputs (shown in fig. S18 in the Supplementary Information) are always in `didv_******.png` files\r\n\r\nReferences:\r\n\r\n[1] V. Blum et al., Ab initio molecular simulations with numeric atom-centered orbitals. Comput. Phys. Commun. 180, 2175-2196 (2009).\r\n\r\n[2] J. Heyd, G. E. Scuseria, M. Ernzerhof, Hybrid functionals based on a screened Coulomb potential. J. Chem. Phys. 118, 8207-8215 (2003).\r\n\r\n[3] A. Tkatchenko, R. A. DiStasio Jr., R. Car, M. Scheffler, Accurate and Efficient Method for Many-Body van der Waals Interactions. Phys. Rev. Lett. 8 108, 236402 (2012).\r\n\r\n[4] P. Hapala et al., Mechanism of high-resolution STM/AFM imaging with functionalized tips. Phys. Rev. B 90, 085421 (2014); P. Hapala, R. Temirov, F. S. Tautz, P. Jelínek, Origin of high-resolution IETS-STM images of organic molecules with functionalized tips. Phys. Rev. Lett. 113, 226101 (2014); https://github.com/ProkopHapala/ProbeParticleModel .\r\n\r\n[5] O. Krejčí, P. Hapala, M. Ondráček, P. Jelínek, Principles and simulations of high-resolution STM imaging with a flexible tip apex. Phys. Rev. B 95, 045407 (2017); https://github.com/ondrejkrejci/PPSTM ."
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