Package: lobsterpy
Getting startedLobsterPy is a package that enables automatic analysis of LOBSTER outputs to get summarized bonding information and relevant bond plots. Additionally, one can also generate features for machine learning studies from LOBSTER outputs. One can download LOBSTER fromhttp://www.cohp.de.ImportantRecently releasedLOBSTER 5.0now generatesPOSCAR.lobsterfor any kind of LOBSTER calculation by default (This file has same format as the POSCAR from VASP). Thus, LobsterPy in principle, now supports usage withallDFT codes supported by LOBSTER and isnolonger limited toVASP. Almost all of the core functionalities of LobsterPy could be used. The user must usePOSCAR.lobsterforpath_to_poscarand-fstructargument in python and cli interface, respectively.The only functionality limited to VASP is DOS comparisons and basis set analysis in thecalc_quality_summarymethod of theAnalysisclass, as it relies on VASP output files, namelyvasprun.xmlandPOTCAR.Please note that LobsterPy relies on the LOBSTER computation output files. Thus, it will be only able to analyze data that has been computed in the LOBSTER run.InstallationStandard installationInstall usingpip install lobsterpyInstallation with featurizerInstall usingpip install lobsterpy[featurizer]Basic usageAutomatic analysis and plotting of COHPs / COBIS / COOPs:You can uselobsterpy descriptionfor an automated analysis of COHPs for relevant cation-anion bonds orlobsterpy automatic-plotto plot the results automatically.
It will evaluate all COHPs with ICOHP values down to 10% of the strongest ICOHP.
You can enforce an analysis of all bonds by usinglobsterpy automatic-plot --allbonds.
You can also switch the automatic analysis to use the ICOBIs or ICOOPs. You need to add--cobisor--coopsalong with the mentioned commands
for e.g.likelobsterpy description --cobisAn interactive plotter is available vialobsterpy automatic-plot-ia.Currently, the computed Mulliken charges will be used to determine cations and anions. If noCHARGE.lobsteris available, the algorithm will fall back to the BondValence analysis from pymatgen.Please be aware that LobsterPy can only analyze bonds that have been included in the initial Lobster computation. Thus, please use the cohpgenerator within Lobster.It is also possible to start this automatic analysis from a Python script. See "examples" for scripts.Plotting DOS from LOBSTER computations:To plot densities of states obtained from LOBSTER uselobsterpy plot-dos.Generic COHP/ COOP / COBI plotter:We included options to plot COHPs/COBIs/COOPs from the command line.lobsterpy plot 1 2will plot COHPs of the first and second bond fromCOHPCAR.lobster. It is possible to sum or integrate the COHPs as well (--summed,--integrated). You can switch to COBIs or COOPs by using--cobisor--coops, respectively.Other command line tools:lobsterpy create-inputswill create standard inputs based on existing POSCAR, POTCAR, and INCAR files. It will allow testing for different basis sets that are available in Lobster. This feature is currently only available for PBE_54 POTCARs, as only the pbeVASPfit2015 basis in LOBSTER that has been fitted to PBE POTCARs includes additional orbitals relevant to solid-state materials. Please
check out our publicationhttps://doi.org/10.1002/cplu.202200123and LOBSTER program manual for more informationFurther help?You can get further information by usinglobsterpy --helpand also by typinglobsterpy description --help,lobsterpy automatic-plot --help,lobsterpy plot --help.Comprehensive documentationCheckout thedocumentation and tutorialsfor more details.ContributingA short guide to contributing to lobsterpy can be foundhere.
Additional information for developers can be foundhere.How to cite?Please cite our paper: J. George, G. Petretto, A. Naik, M. Esters, A. J. Jackson, R. Nelson, R. Dronskowski, G.-M. Rignanese, G. Hautier,ChemPlusChem,https://doi.org/10.1002/cplu.202200123.
Please citepymatgen,Lobster, andChemEnvcorrectly as well.LobsterPy is now a part of an atomate2 workflowWe have now also included the automatic analysis into a fully automatic workflow using VASP and Lobster inatomate2. More documentation and information will follow soon.AcknowledgementsThe development of the program has been supported by a computing time grant. We gratefully acknowledge the Gauss Centre for Supercomputing e.V.(www.gauss-centre.eu) for funding this project by providing computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Centre (www.lrz.de) (project pn73da).