{"id":"https://openalex.org/W7162765823","doi":"https://doi.org/10.48550/arxiv.2605.29446","title":"CrystalXRD-Bench: Benchmarking Vision-Language Models for XRD Peak Indexing Across Diverse Crystalline Materials","display_name":"CrystalXRD-Bench: Benchmarking Vision-Language Models for XRD Peak Indexing Across Diverse Crystalline Materials","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162765823","doi":"https://doi.org/10.48550/arxiv.2605.29446"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29446","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.29446","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127310476","display_name":"Chengliang Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Chengliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137346975","display_name":"Xiaogang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaogang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137384137","display_name":"Peiyao Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Peiyao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137372284","display_name":"Beng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Beng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137329095","display_name":"Hu Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137311256","display_name":"Bing Zhao","orcid":"https://orcid.org/0009-0008-6564-9587"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Bing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.7360000014305115,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.7360000014305115,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12613","display_name":"X-ray Diffraction in Crystallography","score":0.20229999721050262,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11278","display_name":"Calcium Carbonate Crystallization and Inhibition","score":0.010400000028312206,"subfield":{"id":"https://openalex.org/subfields/2502","display_name":"Biomaterials"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.8860999941825867},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.7971000075340271},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7389000058174133},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5640000104904175},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4616999924182892},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.4602000117301941},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.42719998955726624},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.42149999737739563}],"concepts":[{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.8860999941825867},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7971000075340271},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7389000058174133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5795999765396118},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5640000104904175},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5224000215530396},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42719998955726624},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.42149999737739563},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.41530001163482666},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3677000105381012},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.30730000138282776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C459310","wikidata":"https://www.wikidata.org/wiki/Q117801","display_name":"Computational science","level":1,"score":0.2827000021934509},{"id":"https://openalex.org/C2778473407","wikidata":"https://www.wikidata.org/wiki/Q1459574","display_name":"Compendium","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C8010536","wikidata":"https://www.wikidata.org/wiki/Q160398","display_name":"Crystallography","level":1,"score":0.27810001373291016},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2761000096797943},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26919999718666077},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29446","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.29446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29446","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Miller-index":[0],"identification":[1],"from":[2,21,42,125],"powder":[3],"XRD":[4,64,71],"patterns":[5,128],"requires":[6],"capabilities":[7],"untested":[8],"by":[9,92,140],"existing":[10],"multimodal":[11],"benchmarks:":[12],"the":[13,52,59,69,74,121,151,159,162],"model":[14,157],"must":[15],"read":[16],"a":[17,22,38,48],"narrow":[18],"peak":[19,61],"location":[20],"rendered":[23,70],"scientific":[24,171],"curve":[25],"and":[26,78,85,143,175],"then":[27],"connect":[28],"that":[29],"observation":[30],"to":[31,58,145],"multi-step":[32],"crystallographic":[33,45,154],"reasoning.":[34],"We":[35,94],"introduce":[36],"CrystalXRD-Bench,":[37],"250-sample":[39],"benchmark":[40,160],"built":[41],"10":[43],"public":[44],"databases":[46],"for":[47],"single":[49],"task:":[50],"recover":[51],"full":[53],"set":[54],"of":[55,110,114],"HKLs":[56],"contributing":[57],"highest-intensity":[60],"in":[62,153],"an":[63,107],"pattern.":[65],"Each":[66],"sample":[67],"pairs":[68],"image":[72],"with":[73,106],"source":[75],"CIF":[76,146],"text":[77,147],"chemical":[79],"formula,":[80],"so":[81],"visual":[82],"extraction":[83],"errors":[84,87],"reasoning":[86],"can":[88],"be":[89,179],"examined":[90],"side":[91],"side.":[93],"evaluate":[95],"seven":[96,115],"vision-language":[97],"models.":[98],"The":[99],"best":[100],"Jaccard":[101,119],"score":[102],"is":[103,123],"0.5888":[104],"(GPT-5.4)":[105],"exact-match":[108],"rate":[109],"37.6%,":[111],"yet":[112],"six":[113],"models":[116,137],"remain":[117],"below":[118],"0.50;":[120],"task":[122],"far":[124],"solved.":[126],"Error":[127],"vary":[129],"systematically:":[130],"double-peak":[131],"cases":[132],"are":[133],"especially":[134],"brittle,":[135],"recall-heavy":[136],"gain":[138],"coverage":[139],"over-predicting":[141],"HKLs,":[142],"access":[144],"does":[148],"not":[149],"close":[150],"gap":[152],"calculation.":[155],"Alongside":[156],"rankings,":[158],"identifies":[161],"conditions":[163],"under":[164],"which":[165],"current":[166],"VLMs":[167],"fail":[168],"on":[169],"quantitative":[170],"figures.":[172],"All":[173],"data":[174],"evaluation":[176],"code":[177],"will":[178],"publicly":[180],"available.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}
