Semantic mask-based two-step approach: a general framework for X-ray diffraction peak search in high-throughput molecular sieve synthetic system Article Swipe
Zhangpeng Wei
,
Peng Xin
,
Wenli Du
,
Qian Feng
,
Zhiqing Yuan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-024-01396-1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-024-01396-1
X-ray diffraction (XRD) is used for characterizing the crystal structure of molecular sieves after synthetic experiments. However, for a high-throughput molecular sieve synthetic system, the huge amount of data derived from large throughput capacity makes it difficult to analyze timely. While the kernel step of XRD analysis is to search peaks, an automatic way for peak search is needed. Thus, we proposed a novel semantic mask-based two-step framework for peak search in XRD patterns: (1) mask generation, we proposed a multi-resolution net (MRN) to classify the data points of XRD patterns into binary masks (peak/background). (2) Peak search, based on the generated masks, the background points are used to fit an n-order polynomial background curve and estimate the random noises in XRD patterns. Then we proposed three rules named mask, shape, and intensity to screening peaks from initial peak candidates generated by maximum search. Besides, a voting strategy is proposed in peak screening to obtain a precise peak search result. Experiments show that the proposed MRN achieves the state-of-the-art performance compared with other semantic segmentation methods and the proposed peak search method performs better than Jade when using f1 score as the evaluation index.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40747-024-01396-1
- https://link.springer.com/content/pdf/10.1007/s40747-024-01396-1.pdf
- OA Status
- gold
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396933494
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396933494Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s40747-024-01396-1Digital Object Identifier
- Title
-
Semantic mask-based two-step approach: a general framework for X-ray diffraction peak search in high-throughput molecular sieve synthetic systemWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-15Full publication date if available
- Authors
-
Zhangpeng Wei, Peng Xin, Wenli Du, Qian Feng, Zhiqing YuanList of authors in order
- Landing page
-
https://doi.org/10.1007/s40747-024-01396-1Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s40747-024-01396-1.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s40747-024-01396-1.pdfDirect OA link when available
- Concepts
-
Throughput, Computer science, Algorithm, Kernel (algebra), Binary number, Pattern recognition (psychology), Materials science, Artificial intelligence, Mathematics, Telecommunications, Arithmetic, Combinatorics, WirelessTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4396933494 |
|---|---|
| doi | https://doi.org/10.1007/s40747-024-01396-1 |
| ids.doi | https://doi.org/10.1007/s40747-024-01396-1 |
| ids.openalex | https://openalex.org/W4396933494 |
| fwci | 0.0 |
| type | article |
| title | Semantic mask-based two-step approach: a general framework for X-ray diffraction peak search in high-throughput molecular sieve synthetic system |
| awards[0].id | https://openalex.org/G7054766777 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 61803157 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 4 |
| biblio.volume | 10 |
| biblio.last_page | 5614 |
| biblio.first_page | 5599 |
| topics[0].id | https://openalex.org/T11948 |
| topics[0].field.id | https://openalex.org/fields/25 |
| topics[0].field.display_name | Materials Science |
| topics[0].score | 0.9977999925613403 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2505 |
| topics[0].subfield.display_name | Materials Chemistry |
| topics[0].display_name | Machine Learning in Materials Science |
| topics[1].id | https://openalex.org/T12613 |
| topics[1].field.id | https://openalex.org/fields/25 |
| topics[1].field.display_name | Materials Science |
| topics[1].score | 0.9933000206947327 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2505 |
| topics[1].subfield.display_name | Materials Chemistry |
| topics[1].display_name | X-ray Diffraction in Crystallography |
| topics[2].id | https://openalex.org/T10836 |
| topics[2].field.id | https://openalex.org/fields/13 |
| topics[2].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[2].score | 0.9628999829292297 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1312 |
| topics[2].subfield.display_name | Molecular Biology |
| topics[2].display_name | Metabolomics and Mass Spectrometry Studies |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 1320 |
| apc_list.currency | GBP |
| apc_list.value_usd | 1619 |
| apc_paid.value | 1320 |
| apc_paid.currency | GBP |
| apc_paid.value_usd | 1619 |
| concepts[0].id | https://openalex.org/C157764524 |
| concepts[0].level | 3 |
| concepts[0].score | 0.5569489002227783 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1383412 |
| concepts[0].display_name | Throughput |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5457363724708557 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C11413529 |
| concepts[2].level | 1 |
| concepts[2].score | 0.49596866965293884 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[2].display_name | Algorithm |
| concepts[3].id | https://openalex.org/C74193536 |
| concepts[3].level | 2 |
| concepts[3].score | 0.471749871969223 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q574844 |
| concepts[3].display_name | Kernel (algebra) |
| concepts[4].id | https://openalex.org/C48372109 |
| concepts[4].level | 2 |
| concepts[4].score | 0.43883028626441956 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3913 |
| concepts[4].display_name | Binary number |
| concepts[5].id | https://openalex.org/C153180895 |
| concepts[5].level | 2 |
| concepts[5].score | 0.326168954372406 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[5].display_name | Pattern recognition (psychology) |
| concepts[6].id | https://openalex.org/C192562407 |
| concepts[6].level | 0 |
| concepts[6].score | 0.32330310344696045 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[6].display_name | Materials science |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.28515031933784485 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.2633717656135559 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C76155785 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[9].display_name | Telecommunications |
| concepts[10].id | https://openalex.org/C94375191 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11205 |
| concepts[10].display_name | Arithmetic |
| concepts[11].id | https://openalex.org/C114614502 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q76592 |
| concepts[11].display_name | Combinatorics |
| concepts[12].id | https://openalex.org/C555944384 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q249 |
| concepts[12].display_name | Wireless |
| keywords[0].id | https://openalex.org/keywords/throughput |
| keywords[0].score | 0.5569489002227783 |
| keywords[0].display_name | Throughput |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5457363724708557 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/algorithm |
| keywords[2].score | 0.49596866965293884 |
| keywords[2].display_name | Algorithm |
| keywords[3].id | https://openalex.org/keywords/kernel |
| keywords[3].score | 0.471749871969223 |
| keywords[3].display_name | Kernel (algebra) |
| keywords[4].id | https://openalex.org/keywords/binary-number |
| keywords[4].score | 0.43883028626441956 |
| keywords[4].display_name | Binary number |
| keywords[5].id | https://openalex.org/keywords/pattern-recognition |
| keywords[5].score | 0.326168954372406 |
| keywords[5].display_name | Pattern recognition (psychology) |
| keywords[6].id | https://openalex.org/keywords/materials-science |
| keywords[6].score | 0.32330310344696045 |
| keywords[6].display_name | Materials science |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.28515031933784485 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.2633717656135559 |
| keywords[8].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1007/s40747-024-01396-1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S3035462843 |
| locations[0].source.issn | 2198-6053, 2199-4536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2198-6053 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Complex & Intelligent Systems |
| locations[0].source.host_organization | https://openalex.org/P4310319900 |
| locations[0].source.host_organization_name | Springer Science+Business Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s40747-024-01396-1.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Complex & Intelligent Systems |
| locations[0].landing_page_url | https://doi.org/10.1007/s40747-024-01396-1 |
| locations[1].id | pmh:oai:doaj.org/article:df9129fb552d43f4bc40eb3d47a304eb |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Complex & Intelligent Systems, Vol 10, Iss 4, Pp 5599-5614 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/df9129fb552d43f4bc40eb3d47a304eb |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5082885700 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3703-1674 |
| authorships[0].author.display_name | Zhangpeng Wei |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I143593769 |
| authorships[0].affiliations[0].raw_affiliation_string | Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China |
| authorships[0].institutions[0].id | https://openalex.org/I143593769 |
| authorships[0].institutions[0].ror | https://ror.org/01vyrm377 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I143593769 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | East China University of Science and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zhangpeng Wei |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China |
| authorships[1].author.id | https://openalex.org/A5101949303 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0535-728X |
| authorships[1].author.display_name | Peng Xin |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I143593769 |
| authorships[1].affiliations[0].raw_affiliation_string | Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China |
| authorships[1].institutions[0].id | https://openalex.org/I143593769 |
| authorships[1].institutions[0].ror | https://ror.org/01vyrm377 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I143593769 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | East China University of Science and Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xin Peng |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China |
| authorships[2].author.id | https://openalex.org/A5048267931 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2676-6341 |
| authorships[2].author.display_name | Wenli Du |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].raw_affiliation_string | Qingyuan Innovation Laboratory, 1 Qianhuangzhen Road, Quanzhou, 362801, China |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I143593769 |
| authorships[2].affiliations[1].raw_affiliation_string | State Key Laboratory of Industrial Control Technology, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China |
| authorships[2].institutions[0].id | https://openalex.org/I143593769 |
| authorships[2].institutions[0].ror | https://ror.org/01vyrm377 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I143593769 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | East China University of Science and Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Wenli Du |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Qingyuan Innovation Laboratory, 1 Qianhuangzhen Road, Quanzhou, 362801, China, State Key Laboratory of Industrial Control Technology, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China |
| authorships[3].author.id | https://openalex.org/A5102224294 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-7048-1239 |
| authorships[3].author.display_name | Qian Feng |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].raw_affiliation_string | Qingyuan Innovation Laboratory, 1 Qianhuangzhen Road, Quanzhou, 362801, China |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I143593769 |
| authorships[3].affiliations[1].raw_affiliation_string | State Key Laboratory of Industrial Control Technology, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China |
| authorships[3].institutions[0].id | https://openalex.org/I143593769 |
| authorships[3].institutions[0].ror | https://ror.org/01vyrm377 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I143593769 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | East China University of Science and Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Feng Qian |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Qingyuan Innovation Laboratory, 1 Qianhuangzhen Road, Quanzhou, 362801, China, State Key Laboratory of Industrial Control Technology, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China |
| authorships[4].author.id | https://openalex.org/A5089961339 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-9789-7082 |
| authorships[4].author.display_name | Zhiqing Yuan |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I106994412, https://openalex.org/I4210092350 |
| authorships[4].affiliations[0].raw_affiliation_string | State Key Laboratory of Green Chemical Engineering and Industrial Catalysts, Sinopec Shanghai Research Institute of Petrochemical Technology, 1658 North Pudong Road, Shanghai, 201208, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210092350 |
| authorships[4].institutions[0].ror | https://ror.org/00ex3dr91 |
| authorships[4].institutions[0].type | facility |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210092350 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Shanghai Research Institute of Chemical Industry |
| authorships[4].institutions[1].id | https://openalex.org/I106994412 |
| authorships[4].institutions[1].ror | https://ror.org/0161q6d74 |
| authorships[4].institutions[1].type | company |
| authorships[4].institutions[1].lineage | https://openalex.org/I106994412 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | Sinopec (China) |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Zhiqing Yuan |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | State Key Laboratory of Green Chemical Engineering and Industrial Catalysts, Sinopec Shanghai Research Institute of Petrochemical Technology, 1658 North Pudong Road, Shanghai, 201208, China |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s40747-024-01396-1.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Semantic mask-based two-step approach: a general framework for X-ray diffraction peak search in high-throughput molecular sieve synthetic system |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11948 |
| primary_topic.field.id | https://openalex.org/fields/25 |
| primary_topic.field.display_name | Materials Science |
| primary_topic.score | 0.9977999925613403 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2505 |
| primary_topic.subfield.display_name | Materials Chemistry |
| primary_topic.display_name | Machine Learning in Materials Science |
| related_works | https://openalex.org/W2051487156, https://openalex.org/W2073681303, https://openalex.org/W2136583354, https://openalex.org/W2111238207, https://openalex.org/W2053286651, https://openalex.org/W2181743346, https://openalex.org/W2187401768, https://openalex.org/W2181413294, https://openalex.org/W2760721665, https://openalex.org/W2052122378 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1007/s40747-024-01396-1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S3035462843 |
| best_oa_location.source.issn | 2198-6053, 2199-4536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2198-6053 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Complex & Intelligent Systems |
| best_oa_location.source.host_organization | https://openalex.org/P4310319900 |
| best_oa_location.source.host_organization_name | Springer Science+Business Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s40747-024-01396-1.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Complex & Intelligent Systems |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s40747-024-01396-1 |
| primary_location.id | doi:10.1007/s40747-024-01396-1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S3035462843 |
| primary_location.source.issn | 2198-6053, 2199-4536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2198-6053 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Complex & Intelligent Systems |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s40747-024-01396-1.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Complex & Intelligent Systems |
| primary_location.landing_page_url | https://doi.org/10.1007/s40747-024-01396-1 |
| publication_date | 2024-05-15 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2081334718, https://openalex.org/W2003028951, https://openalex.org/W2164782663, https://openalex.org/W3157597489, https://openalex.org/W2063410170, https://openalex.org/W3006497735, https://openalex.org/W2330757026, https://openalex.org/W3039498520, https://openalex.org/W1974460225, https://openalex.org/W1995948118, https://openalex.org/W2022576048, https://openalex.org/W2738319442, https://openalex.org/W4318917672, https://openalex.org/W2054921258, https://openalex.org/W2091998550, https://openalex.org/W1997514070, https://openalex.org/W3119154513, https://openalex.org/W3091169682, https://openalex.org/W1903029394, https://openalex.org/W6600007113, https://openalex.org/W2412782625, https://openalex.org/W2963881378, https://openalex.org/W1901129140, https://openalex.org/W2884436604, https://openalex.org/W3015788359, https://openalex.org/W3200084616, https://openalex.org/W2293255751, https://openalex.org/W1984913489, https://openalex.org/W2281745956, https://openalex.org/W2615257650, https://openalex.org/W2086940198, https://openalex.org/W2121276709, https://openalex.org/W1156864866, https://openalex.org/W1999485892, https://openalex.org/W2053952182, https://openalex.org/W1990622320, https://openalex.org/W2032501495, https://openalex.org/W2949808589, https://openalex.org/W2585656730, https://openalex.org/W2268286716 |
| referenced_works_count | 40 |
| abstract_inverted_index.a | 19, 63, 80, 146, 156 |
| abstract_inverted_index.an | 52, 111 |
| abstract_inverted_index.as | 191 |
| abstract_inverted_index.by | 142 |
| abstract_inverted_index.f1 | 189 |
| abstract_inverted_index.in | 72, 121, 151 |
| abstract_inverted_index.is | 4, 48, 58, 149 |
| abstract_inverted_index.it | 36 |
| abstract_inverted_index.of | 11, 28, 45, 89 |
| abstract_inverted_index.on | 100 |
| abstract_inverted_index.to | 38, 49, 84, 109, 134, 154 |
| abstract_inverted_index.we | 61, 78, 125 |
| abstract_inverted_index.(1) | 75 |
| abstract_inverted_index.(2) | 96 |
| abstract_inverted_index.MRN | 166 |
| abstract_inverted_index.XRD | 46, 73, 90, 122 |
| abstract_inverted_index.and | 116, 132, 177 |
| abstract_inverted_index.are | 107 |
| abstract_inverted_index.fit | 110 |
| abstract_inverted_index.for | 6, 18, 55, 69 |
| abstract_inverted_index.net | 82 |
| abstract_inverted_index.the | 8, 25, 42, 86, 101, 104, 118, 164, 168, 178, 192 |
| abstract_inverted_index.way | 54 |
| abstract_inverted_index.Jade | 186 |
| abstract_inverted_index.Peak | 97 |
| abstract_inverted_index.Then | 124 |
| abstract_inverted_index.data | 29, 87 |
| abstract_inverted_index.from | 31, 137 |
| abstract_inverted_index.huge | 26 |
| abstract_inverted_index.into | 92 |
| abstract_inverted_index.mask | 76 |
| abstract_inverted_index.peak | 56, 70, 139, 152, 158, 180 |
| abstract_inverted_index.show | 162 |
| abstract_inverted_index.step | 44 |
| abstract_inverted_index.than | 185 |
| abstract_inverted_index.that | 163 |
| abstract_inverted_index.used | 5, 108 |
| abstract_inverted_index.when | 187 |
| abstract_inverted_index.with | 172 |
| abstract_inverted_index.(MRN) | 83 |
| abstract_inverted_index.(XRD) | 3 |
| abstract_inverted_index.Thus, | 60 |
| abstract_inverted_index.While | 41 |
| abstract_inverted_index.X-ray | 1 |
| abstract_inverted_index.after | 14 |
| abstract_inverted_index.based | 99 |
| abstract_inverted_index.curve | 115 |
| abstract_inverted_index.large | 32 |
| abstract_inverted_index.makes | 35 |
| abstract_inverted_index.mask, | 130 |
| abstract_inverted_index.masks | 94 |
| abstract_inverted_index.named | 129 |
| abstract_inverted_index.novel | 64 |
| abstract_inverted_index.other | 173 |
| abstract_inverted_index.peaks | 136 |
| abstract_inverted_index.rules | 128 |
| abstract_inverted_index.score | 190 |
| abstract_inverted_index.sieve | 22 |
| abstract_inverted_index.three | 127 |
| abstract_inverted_index.using | 188 |
| abstract_inverted_index.amount | 27 |
| abstract_inverted_index.better | 184 |
| abstract_inverted_index.binary | 93 |
| abstract_inverted_index.index. | 194 |
| abstract_inverted_index.kernel | 43 |
| abstract_inverted_index.masks, | 103 |
| abstract_inverted_index.method | 182 |
| abstract_inverted_index.noises | 120 |
| abstract_inverted_index.obtain | 155 |
| abstract_inverted_index.peaks, | 51 |
| abstract_inverted_index.points | 88, 106 |
| abstract_inverted_index.random | 119 |
| abstract_inverted_index.search | 50, 57, 71, 159, 181 |
| abstract_inverted_index.shape, | 131 |
| abstract_inverted_index.sieves | 13 |
| abstract_inverted_index.voting | 147 |
| abstract_inverted_index.analyze | 39 |
| abstract_inverted_index.crystal | 9 |
| abstract_inverted_index.derived | 30 |
| abstract_inverted_index.initial | 138 |
| abstract_inverted_index.maximum | 143 |
| abstract_inverted_index.methods | 176 |
| abstract_inverted_index.n-order | 112 |
| abstract_inverted_index.needed. | 59 |
| abstract_inverted_index.precise | 157 |
| abstract_inverted_index.result. | 160 |
| abstract_inverted_index.search, | 98 |
| abstract_inverted_index.search. | 144 |
| abstract_inverted_index.system, | 24 |
| abstract_inverted_index.timely. | 40 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Besides, | 145 |
| abstract_inverted_index.However, | 17 |
| abstract_inverted_index.achieves | 167 |
| abstract_inverted_index.analysis | 47 |
| abstract_inverted_index.capacity | 34 |
| abstract_inverted_index.classify | 85 |
| abstract_inverted_index.compared | 171 |
| abstract_inverted_index.estimate | 117 |
| abstract_inverted_index.patterns | 91 |
| abstract_inverted_index.performs | 183 |
| abstract_inverted_index.proposed | 62, 79, 126, 150, 165, 179 |
| abstract_inverted_index.semantic | 65, 174 |
| abstract_inverted_index.strategy | 148 |
| abstract_inverted_index.two-step | 67 |
| abstract_inverted_index.automatic | 53 |
| abstract_inverted_index.difficult | 37 |
| abstract_inverted_index.framework | 68 |
| abstract_inverted_index.generated | 102, 141 |
| abstract_inverted_index.intensity | 133 |
| abstract_inverted_index.molecular | 12, 21 |
| abstract_inverted_index.patterns. | 123 |
| abstract_inverted_index.patterns: | 74 |
| abstract_inverted_index.screening | 135, 153 |
| abstract_inverted_index.structure | 10 |
| abstract_inverted_index.synthetic | 15, 23 |
| abstract_inverted_index.background | 105, 114 |
| abstract_inverted_index.candidates | 140 |
| abstract_inverted_index.evaluation | 193 |
| abstract_inverted_index.mask-based | 66 |
| abstract_inverted_index.polynomial | 113 |
| abstract_inverted_index.throughput | 33 |
| abstract_inverted_index.Experiments | 161 |
| abstract_inverted_index.diffraction | 2 |
| abstract_inverted_index.generation, | 77 |
| abstract_inverted_index.performance | 170 |
| abstract_inverted_index.experiments. | 16 |
| abstract_inverted_index.segmentation | 175 |
| abstract_inverted_index.characterizing | 7 |
| abstract_inverted_index.high-throughput | 20 |
| abstract_inverted_index.multi-resolution | 81 |
| abstract_inverted_index.state-of-the-art | 169 |
| abstract_inverted_index.(peak/background). | 95 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.06008456 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |