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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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"en": "Here are four *.zip files containing all the necessary data for recalculation of the PPM (PP-AFM) [see ref.] images for all the four theoretical structures ( A1 , A2 , A3 and A4 ) as they are published in the manuscriptr: \r\n\r\nJ. Järvi, .B Alldritt, O. Krejcí, .M Todorovic, P. Liljeroth and P. Rinke\r\n\r\nIntegrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations\r\n\r\narXiv preprint arXiv:2104.05302\r\n\r\n \r\n\r\nIn all these zip files you can find the exact pathway for reconstruction of the data as welll as the published results as saved in the separated README.txt files:\r\n\r\n******************************************************************************************************************************************************************\r\n\r\nAtomic geometry of the system is saved in a FHI-AIMS format in geometry.in file.\r\n\r\nThe Hartree potential was calculated through the FHI-AIMS using the options and settings saved in control.in file \r\n\r\nThe Hartree potential is stored in cube_001_hartree_potential.cube file.\r\n\r\n \r\n\r\nPPM (PP-AFM) simulations done with PP-AFM code: https://github.com/ProkopHapala/ProbeParticleModel \r\n\r\n>>>\r\n\r\n>>> commit 76f6104d52b9ce44b6bbf76ed96846bcc5433c96 (HEAD -> master, origin/master, origin/HEAD)\r\n\r\n>>> Author: Krejci Ondrej <krejcio1@t30400-la055.org.aalto.fi>\r\n\r\n>>> Date: Wed Mar 25 15:31:14 2020 +020\r\n\r\n \r\n\r\nThe options and setting from the PPM (PP-AFM) run are saved in params.ini file.\r\n\r\nThe PPM (PP-AFM) run was done through `./run_ppafm.sh` ; the run_ppafm bash file contains all 4 necessary steps for producing the simulated CO-AFM images.\r\n\r\nThe PP-AFM code uses the geometry saved in the cube_001_hartree_potential.cube file as it shifts the coordinates according to the origin of the cube file.\r\n\r\n \r\n\r\nThe images used for this publication are in the *.png files. The height stated in the *.png images is agains 0.0; The height against the heighest atom of the\r\n\r\nstructure was calculated through coordinates stored in geometry.in (the error caused by the non 0. 0. 0. of the cube file is lower than 0.05 Angstroms) file.\r\n\r\nThe final manuscript images were obtained via gwyddion ( http://gwyddion.net ) from the WSxM format of the data obtained through running the following command:\r\n\r\n`python PPM_Master/plot_results.py --WSxM --npy` in the same directory. The WSxM data are stored in *.xyz files.\r\n\r\n******************************************************************************************************************************************************************\r\n\r\n \r\n\r\nThe input data and results (together with the procedure) of the statistical analysis of all the measured angles and lengths for all the experimental and simulation data can be found in:\r\n\r\nStat_analysis_final.pdf"
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"en": "*** Matlab codes for optimization under unitary matrix constraint\r\n\r\nAuthor of the codes: Traian Emanuel Abrudan (joint work with Jan Eriksson and Visa Koivunen)\r\nSMARAD CoE, Department of Signal Processing and Acoustics, Aalto Univestity, Espoo, Finland\r\n\r\n\r\n** General description\r\n\r\nThis set of codes may be used to optimize a smooth (differentiable) cost function J (W) over the Lie group of n × n unitary matrices.\r\nThe codes we provide can be used to either minimize or maximize an arbitrary smooth cost function under unitary matrix constraint.\r\n\r\nThe title and description of this software/code correspond with the situation when the software metadata was imported to ACRIS. The most recent version of metadata is available in the original repository.\r\n\r\n\r\n• The following gradient-based optimization algorithms are implemented (see Sections\r\n3.2.1 and 3.2.2, and Tables 3.2 and 3.3 in [1])\r\n\r\n– SD/SA: Steepest Descent/Ascent ,\r\n– CG-PR: Conjugate Gradient (Polak-Ribière formula)\r\n– CG-FR: Conjugate Gradient (Fletcher-Reeves formula)\r\n\r\n• The following line search methods are implemented and used together with the\r\ngradient algorithms (see Section 3.2.3, and Table 3.4 in [1]):\r\n– Armijo step method,\r\n– polynomial approximation-based method\r\n– DFT approximation-based method\r\n\r\nAll combinations of Riemannian gradient methods and line search methods proposed\r\nin [1] are tested. In practical applications, just one of these algorithms (SD/SA, CG-\r\nPR/CG-FR) together with one of the line search methods (Armijo, polynomial of DFT-\r\nbased) is sufficient to solve the problem at hand. The algorithm/method to be used may\r\nbe chosen based on experimental testing. The difference in performance may depend on\r\nthe cost function. In general, based on our experience, first we recommend CG-PR with\r\nthe polynomial-based line search method, and second, SD/SA with the DFT-based line\r\nsearch method.\r\n\r\nAs a test example, the Brockett cost function (see [1] for details).\r\n\r\nNOTE: This set of codes was designed such that the Riemannian optimization scripts\r\nare separated from the cost function specific scripts. Therefore, these codes can also be\r\neasily used to optimize other smooth cost functions, simply by changing the cost function-\r\nspecific parameters/scripts. The cost function specific parts are: the cost function evalu-\r\nation, the gradient computation and the order of the cost function in the coefficients of\r\nW (details are provided in the next section).\r\n\r\nFor a much more detailed description of the methods, codes and their operation, see README.pdf file.\r\n\r\n** References\r\n\r\n[1] T. Abrudan. “Advanced Optimization Algorithms for Sensor Arrays and Multi-\r\nantenna Communications”, (PhD. thesis) Department of Signal Processing and Acous-\r\ntics, Aalto University, Finland, 21 Nov. 2008.\r\nPDF available at: http://lib.tkk.fi/Diss/2008/isbn9789512296071/\r\n\r\n-------\r\n\r\nBibTeX citation\r\n\r\n@PhdThesis{Abr08_PhD_thesis,\r\nauthor =\r\n{T. Abrudan},\r\ntitle =\r\n{Advanced Optimization Algorithms for Sensor Arrays\r\nand Multi-antenna Communications},\r\nschool =\r\n{Department of Signal Processing and Acoustics,\r\nAalto University, Finland},\r\nyear =\r\n{2008},\r\nmonth =\r\n{21 Nov.},\r\nurl =\r\n{http://lib.tkk.fi/Diss/2008/isbn9789512296071/},\r\n}"
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