{"id":1192061,"date":"2026-10-29T15:54:05","date_gmt":"2026-10-29T22:54:05","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/periodic-gfn2-xtb-in-cp2k-multipolar-ewald-electrostatics-k-point-sampling-and-transferability-benchmarks-for-solids\/"},"modified":"2026-10-29T16:13:51","modified_gmt":"2026-10-29T23:13:51","slug":"periodic-gfn2-xtb-in-cp2k-multipolar-ewald-electrostatics-k-point-sampling-and-transferability-benchmarks-for-solids","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/periodic-gfn2-xtb-in-cp2k-multipolar-ewald-electrostatics-k-point-sampling-and-transferability-benchmarks-for-solids\/","title":{"rendered":"Periodic GFN2\u2011xTB in CP2K: Multipolar Ewald Electrostatics, k\u2011Point Sampling, and Transferability Benchmarks for Solids"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Extended tight-binding methods are attractive for condensed-phase simulations because they retain an explicit electronic Hamiltonian at a cost far below conventional density functional theory. Their transferability to periodic materials, however, cannot be assessed reliably without a genuinely periodic Hamiltonian, Brillouin-zone sampling, and symmetry-aware crystal calculations. We present a periodic implementation of the second-generation geometry, frequency, and noncovalent interaction parametrization of extended tight binding (GFN2-xTB) in CP2K. The central theoretical ingredient is a multipolar Ewald formulation for the self-consistent shell charges, atomic dipoles, and quadrupoles of GFN2-xTB, together with analytical forces and stress contributions for geometry optimization, cell optimization, and molecular dynamics. For the ranking of ice polymorphs in the DMC-ICE13 data set, periodic GFN2-xTB on a converged 33 k-point mesh improves the relative polymorph energies over GFN1-xTB, reducing the mean absolute error from 8.01 to 3.46 kJ mol\u20131. Because GFN1-xTB and GFN2-xTB differ in several coupled, jointly parametrized terms, this whole-model comparison does not isolate the contribution of multipolar electrostatics. For the LC10 set of cubic covalent and ionic solids, GFN2-xTB yields valid equation-of-state minima for all ten systems, with lattice-constant and cohesive-energy mean absolute errors of 0.062 \u00c5 and 1.30 eV per atom, respectively. Periodic GFN2-xTB is evaluated for molecular crystal lattice energies and cell parameters using the X23b data set. Using native Bloch 23-mesh cell optimization with full symmetry reduction, followed by a 33-mesh energy evaluation on the optimized cells, periodic GFN2-xTB gives a mean absolute lattice-energy error of 14.09 kJ mol\u20131 and a mean absolute relaxed-cell volume error of 5.84% for the full X23b relaxed-cell set. The benchmarks position periodic GFN2-xTB as an efficient electronic structure level for screening, pre-optimization, and large exploratory simulations. This work creates the foundation for periodic anisotropic electrostatics in the extended tight-binding framework and their efficient application in CP2K.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Extended tight-binding methods are attractive for condensed-phase simulations because they retain an explicit electronic Hamiltonian at a cost far below conventional density functional theory. Their transferability to periodic materials, however, cannot be assessed reliably without a genuinely periodic Hamiltonian, Brillouin-zone sampling, and symmetry-aware crystal calculations. We present a periodic implementation of the second-generation geometry, frequency, [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Vahideh Alizadeh","user_id":0},{"type":"text","value":"Johann V. Pototschnig","user_id":0},{"type":"text","value":"L. M. Seidler","user_id":0},{"type":"user_nicename","value":"Sebastian Ehlert","user_id":"42804"},{"type":"text","value":"T. K&uuml;hne","user_id":0}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Journal of Chemical Theory and Computation","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Journal of chemical theory and computation","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"9688","msr_page_range_end":"9699","msr_series":"","msr_volume":"22","msr_copyright":"","msr_conference_name":"","msr_doi":"10.1021\/acs.jctc.6c01034","msr_arxiv_id":"","msr_mag_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_release_tracker_id":"","msr_highlight_type":"","msr_date_display_format":"","msr_main_download_label":"","msr_external_link_label":"","msr_doi_label":"","msr_published_date":"2026-09-08","msr_startdate":"","msr_presentation_date":"","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_year":2026,"msr_month":9,"msr_day":8,"msr_microsoftintellectualproperty":false,"msr_pub_id":"d56121d997e9fa834ac80a6d6bb50840b8a6e8a9","msr_publication_uploader":[{"type":"url","title":"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/13602927","label_id":243109,"id":false,"viewUrl":false},{"type":"url","title":"https:\/\/pubmed.ncbi.nlm.nih.gov\/42917474","label_id":243109,"id":false,"viewUrl":false}],"msr_related_uploader":[],"msr_original_fields_of_study":[],"msr_s2_paper_id":"d56121d997e9fa834ac80a6d6bb50840b8a6e8a9","msr_s2_pdf_url":"https:\/\/doi.org\/10.1021\/acs.jctc.6c01034","msr_citation_count_updated":"","msr_citation_count":2,"msr_influential_citations":0,"msr_reference_count":77,"msr_s2_open_access":true,"msr_s2_author_ids":[],"msr_pub_ids":[{"provider":"s2","id":"d56121d997e9fa834ac80a6d6bb50840b8a6e8a9"},{"provider":"doi","id":"10.1021\/acs.jctc.6c01034"},{"provider":"pubmed","id":"42917474"},{"provider":"pubmedcentral","id":"13602927"},{"provider":"corpusid","id":"291879048"}],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13556,13546,13560],"msr-publication-type":[193715],"msr-publisher":[],"msr-publication-cta":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[246985],"msr-conference":[],"msr-journal":[270283],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-1192061","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-computational-sciences-mathematics","msr-research-area-programming-languages-software-engineering","msr-locale-en_us","msr-field-of-study-medicine"],"msr_publishername":"","msr_edition":"Journal of Chemical Theory and Computation","msr_affiliation":"","msr_published_date":"2026-09-08","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"Journal of chemical theory and computation","msr_volume":"22","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":0,"msr_main_download":"","msr_publicationurl":"","msr_doi":"10.1021\/acs.jctc.6c01034","msr_publication_uploader":[{"type":"url","title":"https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/13602927","label_id":243109,"id":false,"viewUrl":false},{"type":"url","title":"https:\/\/pubmed.ncbi.nlm.nih.gov\/42917474","label_id":243109,"id":false,"viewUrl":false}],"msr_related_uploader":[],"msr_citation_count":2,"msr_citation_count_updated":"","msr_s2_paper_id":"d56121d997e9fa834ac80a6d6bb50840b8a6e8a9","msr_influential_citations":0,"msr_reference_count":77,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":true,"msr_s2_pdf_url":"https:\/\/doi.org\/10.1021\/acs.jctc.6c01034","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Vahideh Alizadeh","user_id":0,"rest_url":false},{"type":"text","value":"Johann V. Pototschnig","user_id":0,"rest_url":false},{"type":"text","value":"L. M. Seidler","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Sebastian Ehlert","user_id":42804,"rest_url":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sebastian Ehlert"},{"type":"text","value":"T. K&uuml;hne","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[851467],"msr_event":[],"msr_group":[],"msr_project":[],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"article","related_content":[],"_links":{"self":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1192061","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":4,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1192061\/revisions"}],"predecessor-version":[{"id":1192118,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/1192061\/revisions\/1192118"}],"wp:attachment":[{"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/media?parent=1192061"}],"wp:term":[{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=1192061"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=1192061"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=1192061"},{"taxonomy":"msr-publisher","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-publisher?post=1192061"},{"taxonomy":"msr-publication-cta","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-cta?post=1192061"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=1192061"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=1192061"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=1192061"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=1192061"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=1192061"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=1192061"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=1192061"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=1192061"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}