M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery Article Swipe
Yuanqi Du
,
Yingheng Wang
,
Yining Huang
,
Jianan Canal Li
,
Yanqiao Zhu
,
Tian Xie
,
Chenru Duan
,
John M. Gregoire
,
Carla P. Gomes
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.05378
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.05378
We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machine learning approaches for modeling materials structures lag behind, which is partly due to the lack of an integrated platform that enables access to diverse tasks for materials discovery. To bridge this gap, M$^2$Hub will enable easy access to materials discovery tasks, datasets, machine learning methods, evaluations, and benchmark results that cover the entire workflow. Specifically, the first release of M$^2$Hub focuses on three key stages in materials discovery: virtual screening, inverse design, and molecular simulation, including 9 datasets that covers 6 types of materials with 56 tasks across 8 types of material properties. We further provide 2 synthetic datasets for the purpose of generative tasks on materials. In addition to random data splits, we also provide 3 additional data partitions to reflect the real-world materials discovery scenarios. State-of-the-art machine learning methods (including those are suitable for materials structures but never compared in the literature) are benchmarked on representative tasks. Our codes and library are publicly available at https://github.com/yuanqidu/M2Hub.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.05378
- https://arxiv.org/pdf/2307.05378
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384115375
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4384115375Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2307.05378Digital Object Identifier
- Title
-
M$^2$Hub: Unlocking the Potential of Machine Learning for Materials DiscoveryWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-14Full publication date if available
- Authors
-
Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John M. Gregoire, Carla P. GomesList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.05378Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2307.05378Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2307.05378Direct OA link when available
- Concepts
-
Workflow, Computer science, Drug discovery, Machine learning, Benchmark (surveying), Key (lock), Artificial intelligence, Bridge (graph theory), Knowledge extraction, Generative grammar, Data science, Database, Bioinformatics, Computer security, Internal medicine, Biology, Medicine, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4384115375 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2307.05378 |
| ids.doi | https://doi.org/10.48550/arxiv.2307.05378 |
| ids.openalex | https://openalex.org/W4384115375 |
| fwci | |
| type | preprint |
| title | M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.9994000196456909 |
| 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/T10211 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9491000175476074 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1703 |
| topics[1].subfield.display_name | Computational Theory and Mathematics |
| topics[1].display_name | Computational Drug Discovery Methods |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C177212765 |
| concepts[0].level | 2 |
| concepts[0].score | 0.742943525314331 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q627335 |
| concepts[0].display_name | Workflow |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7375771999359131 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C74187038 |
| concepts[2].level | 2 |
| concepts[2].score | 0.611068069934845 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1418791 |
| concepts[2].display_name | Drug discovery |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5861133933067322 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C185798385 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5738810896873474 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[4].display_name | Benchmark (surveying) |
| concepts[5].id | https://openalex.org/C26517878 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5738202333450317 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q228039 |
| concepts[5].display_name | Key (lock) |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4834536015987396 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C100776233 |
| concepts[7].level | 2 |
| concepts[7].score | 0.47528234124183655 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2532492 |
| concepts[7].display_name | Bridge (graph theory) |
| concepts[8].id | https://openalex.org/C120567893 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4411984980106354 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1582085 |
| concepts[8].display_name | Knowledge extraction |
| concepts[9].id | https://openalex.org/C39890363 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4409130811691284 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q36108 |
| concepts[9].display_name | Generative grammar |
| concepts[10].id | https://openalex.org/C2522767166 |
| concepts[10].level | 1 |
| concepts[10].score | 0.4227278232574463 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[10].display_name | Data science |
| concepts[11].id | https://openalex.org/C77088390 |
| concepts[11].level | 1 |
| concepts[11].score | 0.13715767860412598 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[11].display_name | Database |
| concepts[12].id | https://openalex.org/C60644358 |
| concepts[12].level | 1 |
| concepts[12].score | 0.10259497165679932 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q128570 |
| concepts[12].display_name | Bioinformatics |
| concepts[13].id | https://openalex.org/C38652104 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[13].display_name | Computer security |
| concepts[14].id | https://openalex.org/C126322002 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[14].display_name | Internal medicine |
| concepts[15].id | https://openalex.org/C86803240 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[15].display_name | Biology |
| concepts[16].id | https://openalex.org/C71924100 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[16].display_name | Medicine |
| concepts[17].id | https://openalex.org/C205649164 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[17].display_name | Geography |
| concepts[18].id | https://openalex.org/C13280743 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q131089 |
| concepts[18].display_name | Geodesy |
| keywords[0].id | https://openalex.org/keywords/workflow |
| keywords[0].score | 0.742943525314331 |
| keywords[0].display_name | Workflow |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7375771999359131 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/drug-discovery |
| keywords[2].score | 0.611068069934845 |
| keywords[2].display_name | Drug discovery |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.5861133933067322 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/benchmark |
| keywords[4].score | 0.5738810896873474 |
| keywords[4].display_name | Benchmark (surveying) |
| keywords[5].id | https://openalex.org/keywords/key |
| keywords[5].score | 0.5738202333450317 |
| keywords[5].display_name | Key (lock) |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.4834536015987396 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/bridge |
| keywords[7].score | 0.47528234124183655 |
| keywords[7].display_name | Bridge (graph theory) |
| keywords[8].id | https://openalex.org/keywords/knowledge-extraction |
| keywords[8].score | 0.4411984980106354 |
| keywords[8].display_name | Knowledge extraction |
| keywords[9].id | https://openalex.org/keywords/generative-grammar |
| keywords[9].score | 0.4409130811691284 |
| keywords[9].display_name | Generative grammar |
| keywords[10].id | https://openalex.org/keywords/data-science |
| keywords[10].score | 0.4227278232574463 |
| keywords[10].display_name | Data science |
| keywords[11].id | https://openalex.org/keywords/database |
| keywords[11].score | 0.13715767860412598 |
| keywords[11].display_name | Database |
| keywords[12].id | https://openalex.org/keywords/bioinformatics |
| keywords[12].score | 0.10259497165679932 |
| keywords[12].display_name | Bioinformatics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2307.05378 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2307.05378 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2307.05378 |
| locations[1].id | doi:10.48550/arxiv.2307.05378 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2307.05378 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5006709853 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2988-0374 |
| authorships[0].author.display_name | Yuanqi Du |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Du, Yuanqi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5054001148 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4828-4757 |
| authorships[1].author.display_name | Yingheng Wang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Wang, Yingheng |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5066432177 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0106-8771 |
| authorships[2].author.display_name | Yining Huang |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Huang, Yining |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5061059626 |
| authorships[3].author.orcid | https://orcid.org/0009-0001-7772-5742 |
| authorships[3].author.display_name | Jianan Canal Li |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Li, Jianan Canal |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5015101640 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2205-5304 |
| authorships[4].author.display_name | Yanqiao Zhu |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Zhu, Yanqiao |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5074254868 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-1083-0978 |
| authorships[5].author.display_name | Tian Xie |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Xie, Tian |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5029457626 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-2592-4237 |
| authorships[6].author.display_name | Chenru Duan |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Duan, Chenru |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5037026533 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-2863-5265 |
| authorships[7].author.display_name | John M. Gregoire |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Gregoire, John M. |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5069030030 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-4441-7225 |
| authorships[8].author.display_name | Carla P. Gomes |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Gomes, Carla P. |
| authorships[8].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2307.05378 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-07-13T00:00:00 |
| display_name | M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| 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 | 0.9994000196456909 |
| 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/W1981780420, https://openalex.org/W2182707996, https://openalex.org/W45233828, https://openalex.org/W2964988449, https://openalex.org/W2397952901, https://openalex.org/W2029380707, https://openalex.org/W2380075625, https://openalex.org/W4255934811, https://openalex.org/W2465382974, https://openalex.org/W2010229520 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2307.05378 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2307.05378 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2307.05378 |
| primary_location.id | pmh:oai:arXiv.org:2307.05378 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2307.05378 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2307.05378 |
| publication_date | 2023-06-14 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.2 | 128 |
| abstract_inverted_index.3 | 148 |
| abstract_inverted_index.6 | 112 |
| abstract_inverted_index.8 | 120 |
| abstract_inverted_index.9 | 108 |
| abstract_inverted_index.a | 3 |
| abstract_inverted_index.56 | 117 |
| abstract_inverted_index.In | 139 |
| abstract_inverted_index.To | 60 |
| abstract_inverted_index.We | 0, 125 |
| abstract_inverted_index.an | 48 |
| abstract_inverted_index.at | 188 |
| abstract_inverted_index.in | 9, 18, 97, 173 |
| abstract_inverted_index.is | 41 |
| abstract_inverted_index.of | 30, 47, 90, 114, 122, 134 |
| abstract_inverted_index.on | 93, 137, 178 |
| abstract_inverted_index.to | 44, 54, 69, 141, 152 |
| abstract_inverted_index.we | 145 |
| abstract_inverted_index.Our | 181 |
| abstract_inverted_index.and | 78, 104, 183 |
| abstract_inverted_index.are | 165, 176, 185 |
| abstract_inverted_index.but | 170 |
| abstract_inverted_index.due | 43 |
| abstract_inverted_index.for | 5, 24, 34, 57, 131, 167 |
| abstract_inverted_index.has | 14 |
| abstract_inverted_index.key | 95 |
| abstract_inverted_index.lag | 38 |
| abstract_inverted_index.the | 28, 45, 83, 87, 132, 154, 174 |
| abstract_inverted_index.also | 146 |
| abstract_inverted_index.data | 143, 150 |
| abstract_inverted_index.drug | 25 |
| abstract_inverted_index.easy | 67 |
| abstract_inverted_index.gap, | 63 |
| abstract_inverted_index.lack | 46 |
| abstract_inverted_index.that | 51, 81, 110 |
| abstract_inverted_index.this | 62 |
| abstract_inverted_index.will | 65 |
| abstract_inverted_index.with | 116 |
| abstract_inverted_index.codes | 182 |
| abstract_inverted_index.cover | 82 |
| abstract_inverted_index.first | 88 |
| abstract_inverted_index.never | 171 |
| abstract_inverted_index.tasks | 56, 118, 136 |
| abstract_inverted_index.those | 164 |
| abstract_inverted_index.three | 94 |
| abstract_inverted_index.types | 113, 121 |
| abstract_inverted_index.which | 40 |
| abstract_inverted_index.access | 53, 68 |
| abstract_inverted_index.across | 119 |
| abstract_inverted_index.bridge | 61 |
| abstract_inverted_index.covers | 111 |
| abstract_inverted_index.enable | 66 |
| abstract_inverted_index.entire | 84 |
| abstract_inverted_index.partly | 42 |
| abstract_inverted_index.random | 142 |
| abstract_inverted_index.stages | 96 |
| abstract_inverted_index.tasks, | 72 |
| abstract_inverted_index.tasks. | 180 |
| abstract_inverted_index.Machine | 12 |
| abstract_inverted_index.behind, | 39 |
| abstract_inverted_index.design, | 103 |
| abstract_inverted_index.diverse | 55 |
| abstract_inverted_index.enables | 52 |
| abstract_inverted_index.focuses | 92 |
| abstract_inverted_index.further | 126 |
| abstract_inverted_index.inverse | 102 |
| abstract_inverted_index.library | 184 |
| abstract_inverted_index.machine | 7, 31, 74, 160 |
| abstract_inverted_index.methods | 162 |
| abstract_inverted_index.provide | 127, 147 |
| abstract_inverted_index.purpose | 133 |
| abstract_inverted_index.reflect | 153 |
| abstract_inverted_index.release | 89 |
| abstract_inverted_index.results | 80 |
| abstract_inverted_index.splits, | 144 |
| abstract_inverted_index.toolkit | 4 |
| abstract_inverted_index.virtual | 100 |
| abstract_inverted_index.However, | 27 |
| abstract_inverted_index.M$^2$Hub | 64, 91 |
| abstract_inverted_index.achieved | 15 |
| abstract_inverted_index.addition | 140 |
| abstract_inverted_index.compared | 172 |
| abstract_inverted_index.datasets | 109, 130 |
| abstract_inverted_index.learning | 8, 13, 32, 75, 161 |
| abstract_inverted_index.material | 123 |
| abstract_inverted_index.methods, | 76 |
| abstract_inverted_index.modeling | 19, 35 |
| abstract_inverted_index.platform | 50 |
| abstract_inverted_index.progress | 17 |
| abstract_inverted_index.publicly | 186 |
| abstract_inverted_index.suitable | 166 |
| abstract_inverted_index.M$^2$Hub, | 2 |
| abstract_inverted_index.advancing | 6 |
| abstract_inverted_index.available | 187 |
| abstract_inverted_index.benchmark | 79 |
| abstract_inverted_index.datasets, | 73 |
| abstract_inverted_index.discovery | 71, 157 |
| abstract_inverted_index.including | 107 |
| abstract_inverted_index.introduce | 1 |
| abstract_inverted_index.materials | 10, 36, 58, 70, 98, 115, 156, 168 |
| abstract_inverted_index.molecular | 20, 105 |
| abstract_inverted_index.synthetic | 129 |
| abstract_inverted_index.workflow. | 85 |
| abstract_inverted_index.(including | 163 |
| abstract_inverted_index.additional | 149 |
| abstract_inverted_index.approaches | 33 |
| abstract_inverted_index.discovery. | 11, 26, 59 |
| abstract_inverted_index.discovery: | 99 |
| abstract_inverted_index.especially | 22 |
| abstract_inverted_index.generative | 135 |
| abstract_inverted_index.integrated | 49 |
| abstract_inverted_index.materials. | 138 |
| abstract_inverted_index.partitions | 151 |
| abstract_inverted_index.real-world | 155 |
| abstract_inverted_index.remarkable | 16 |
| abstract_inverted_index.scenarios. | 158 |
| abstract_inverted_index.screening, | 101 |
| abstract_inverted_index.structures | 37, 169 |
| abstract_inverted_index.benchmarked | 177 |
| abstract_inverted_index.development | 29 |
| abstract_inverted_index.literature) | 175 |
| abstract_inverted_index.properties. | 124 |
| abstract_inverted_index.simulation, | 106 |
| abstract_inverted_index.structures, | 21 |
| abstract_inverted_index.biomolecules | 23 |
| abstract_inverted_index.evaluations, | 77 |
| abstract_inverted_index.Specifically, | 86 |
| abstract_inverted_index.representative | 179 |
| abstract_inverted_index.State-of-the-art | 159 |
| abstract_inverted_index.https://github.com/yuanqidu/M2Hub. | 189 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |