{"id":"https://openalex.org/W4415960217","doi":"https://doi.org/10.48550/arxiv.2510.16897","title":"DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library","display_name":"DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4415960217","doi":"https://doi.org/10.48550/arxiv.2510.16897"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2510.16897","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.16897","pdf_url":"https://arxiv.org/pdf/2510.16897","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2510.16897","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107178963","display_name":"Jose Siguenza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Siguenza, Jose","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5039641760","display_name":"Bharath Ramsundar","orcid":"https://orcid.org/0000-0001-8450-4262"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramsundar, Bharath","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":0.9685999751091003,"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":0.9685999751091003,"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/T12748","display_name":"Molecular spectroscopy and chirality","score":0.007600000128149986,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.003599999938160181,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/equivariant-map","display_name":"Equivariant map","score":0.6920999884605408},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.6065999865531921},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.576200008392334},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5634999871253967},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48669999837875366},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.3946000039577484},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.383899986743927}],"concepts":[{"id":"https://openalex.org/C171036898","wikidata":"https://www.wikidata.org/wiki/Q256355","display_name":"Equivariant map","level":2,"score":0.6920999884605408},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.671999990940094},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.6065999865531921},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5791000127792358},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.576200008392334},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5634999871253967},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48669999837875366},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.3946000039577484},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2702000141143799},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.251800000667572}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2510.16897","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.16897","pdf_url":"https://arxiv.org/pdf/2510.16897","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.16897","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.16897","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2510.16897","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.16897","pdf_url":"https://arxiv.org/pdf/2510.16897","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Neural":[0],"networks":[1],"that":[2],"incorporate":[3],"geometric":[4],"relationships":[5],"respecting":[6],"SE(3)":[7],"group":[8],"transformations":[9],"(e.g.":[10],"rotations":[11],"and":[12,28,58,75,101,107,119,129,135],"translations)":[13],"are":[14],"increasingly":[15],"important":[16],"in":[17],"molecular":[18,22],"applications,":[19],"such":[20,54,104],"as":[21,34,55,105],"property":[23],"prediction,":[24],"protein":[25],"structure":[26],"modeling,":[27],"materials":[29],"design.":[30],"These":[31],"models,":[32,90,103,115],"known":[33],"SE(3)-equivariant":[35,139],"neural":[36],"networks,":[37],"ensure":[38],"outputs":[39],"transform":[40],"predictably":[41],"with":[42,85,93,126],"input":[43],"coordinate":[44],"changes":[45],"by":[46],"explicitly":[47],"encoding":[48],"spatial":[49],"atomic":[50],"positions.":[51],"Although":[52],"libraries":[53],"E3NN":[56],"[4]":[57],"SE(3)-TRANSFORMER":[59],"[3":[60],"]":[61],"offer":[62],"powerful":[63],"implementations,":[64],"they":[65],"often":[66],"require":[67],"substantial":[68],"deep":[69,95],"learning":[70,96],"or":[71],"mathematical":[72],"prior":[73],"knowledge":[74],"lack":[76],"complete":[77,116],"training":[78,117],"pipelines.":[79],"We":[80],"extend":[81],"DEEPCHEM":[82],"[":[83],"13]":[84],"support":[86],"for":[87],"ready-to-use":[88],"equivariant":[89,114,123],"enabling":[91],"scientists":[92],"minimal":[94],"background":[97],"to":[98,131],"build,":[99],"train,":[100],"evaluate":[102],"SE(3)-Transformer":[106],"Tensor":[108],"Field":[109],"Networks.":[110],"Our":[111],"implementation":[112],"includes":[113],"pipelines,":[118],"a":[120],"toolkit":[121],"of":[122,138],"utilities,":[124],"supported":[125],"comprehensive":[127],"tests":[128],"documentation,":[130],"facilitate":[132],"both":[133],"application":[134],"further":[136],"development":[137],"models.":[140]},"counts_by_year":[],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-22T00:00:00"}