Data-driven machine learning models for the quick and accurate prediction of Tg and Td of OLED materials Article Swipe
Yihuan Zhao
,
Caixia Fu
,
Ling Fu
,
Zhiyun Lu
,
Xuemei Pu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2
Organic light-emitting-diode (OLED) materials have exhibited a wide range of applications. However, further development and commercialization of OLEDs requires higher-quality OLED materials, including high thermal stability associated with the glass transition temperature (Tg) and decomposition temperature (Td). Experimental determinations of the two important properties genernally involve a time-consuming and laborious process. Thus, it is highly desired to develop a quick and accurate prediction tool. Motivated by the changelle, we explored machine learning based framework by constructing new dataset with more than one thousand samples collected from a wide range of literaturesm, throngh which ensemble learning models were explored. Models trained with the LightGBM algorithm exhibit the best prediction performance, where the values of MAE, RMSE, and R2 are 17.15 K, 24.63 K, and 0.77 for Tg prediction, 24.91 K, 33.88 K, and 0.78 for Td prediction. The prediction performance and the generalization of the machine learning models are further tested by out-of-sample dataset, also exhibiting satisfactory results. Experimental verification further demonstrates the reliability and the practical potential of the ML-based model. In order to extend the practice application of the ML-based models, an online prediction platform was constructed, including the optimal predition models and all the thermal stability data under study, which are freely available at http://oledtppxmpugroup.com. We expect that they will become a useful tool for experimental investigations on Tg and Td, in turn accelerating the design of the OLED materials with high performance.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60fe8efb393cc9f6374efc64/original/data-driven-machine-learning-models-for-the-quick-and-accurate-prediction-of-tg-and-td-of-oled-materials.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3183521452
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3183521452Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2Digital Object Identifier
- Title
-
Data-driven machine learning models for the quick and accurate prediction of Tg and Td of OLED materialsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-07-26Full publication date if available
- Authors
-
Yihuan Zhao, Caixia Fu, Ling Fu, Zhiyun Lu, Xuemei PuList of authors in order
- Landing page
-
https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2Publisher landing page
- PDF URL
-
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60fe8efb393cc9f6374efc64/original/data-driven-machine-learning-models-for-the-quick-and-accurate-prediction-of-tg-and-td-of-oled-materials.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://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60fe8efb393cc9f6374efc64/original/data-driven-machine-learning-models-for-the-quick-and-accurate-prediction-of-tg-and-td-of-oled-materials.pdfDirect OA link when available
- Concepts
-
OLED, Computer science, Predictive modelling, Machine learning, Stability (learning theory), Artificial intelligence, Generalization, Materials science, Mathematics, Nanotechnology, Mathematical analysis, Layer (electronics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3183521452 |
|---|---|
| doi | https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2 |
| ids.doi | https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2 |
| ids.mag | 3183521452 |
| ids.openalex | https://openalex.org/W3183521452 |
| fwci | 0.0 |
| type | preprint |
| title | Data-driven machine learning models for the quick and accurate prediction of Tg and Td of OLED materials |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10611 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9900000095367432 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Organic Light-Emitting Diodes Research |
| topics[1].id | https://openalex.org/T12238 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.927299976348877 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Green IT and Sustainability |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C150759737 |
| concepts[0].level | 3 |
| concepts[0].score | 0.6246730089187622 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q209593 |
| concepts[0].display_name | OLED |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6134726405143738 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C45804977 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5607450604438782 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7239673 |
| concepts[2].display_name | Predictive modelling |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5570996999740601 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C112972136 |
| concepts[4].level | 2 |
| concepts[4].score | 0.48807990550994873 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7595718 |
| concepts[4].display_name | Stability (learning theory) |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.45350977778434753 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C177148314 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4243262708187103 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q170084 |
| concepts[6].display_name | Generalization |
| concepts[7].id | https://openalex.org/C192562407 |
| concepts[7].level | 0 |
| concepts[7].score | 0.31603771448135376 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[7].display_name | Materials science |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.16165053844451904 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C171250308 |
| concepts[9].level | 1 |
| concepts[9].score | 0.11358803510665894 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11468 |
| concepts[9].display_name | Nanotechnology |
| concepts[10].id | https://openalex.org/C134306372 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[10].display_name | Mathematical analysis |
| concepts[11].id | https://openalex.org/C2779227376 |
| concepts[11].level | 2 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q6505497 |
| concepts[11].display_name | Layer (electronics) |
| keywords[0].id | https://openalex.org/keywords/oled |
| keywords[0].score | 0.6246730089187622 |
| keywords[0].display_name | OLED |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6134726405143738 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/predictive-modelling |
| keywords[2].score | 0.5607450604438782 |
| keywords[2].display_name | Predictive modelling |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.5570996999740601 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/stability |
| keywords[4].score | 0.48807990550994873 |
| keywords[4].display_name | Stability (learning theory) |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.45350977778434753 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/generalization |
| keywords[6].score | 0.4243262708187103 |
| keywords[6].display_name | Generalization |
| keywords[7].id | https://openalex.org/keywords/materials-science |
| keywords[7].score | 0.31603771448135376 |
| keywords[7].display_name | Materials science |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.16165053844451904 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/nanotechnology |
| keywords[9].score | 0.11358803510665894 |
| keywords[9].display_name | Nanotechnology |
| language | en |
| locations[0].id | doi:10.33774/chemrxiv-2021-j5pfd-v2 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60fe8efb393cc9f6374efc64/original/data-driven-machine-learning-models-for-the-quick-and-accurate-prediction-of-tg-and-td-of-oled-materials.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5016574602 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9870-246X |
| authorships[0].author.display_name | Yihuan Zhao |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[0].affiliations[0].raw_affiliation_string | Sichuan University |
| authorships[0].institutions[0].id | https://openalex.org/I24185976 |
| authorships[0].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Sichuan University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yihuan Zhao |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Sichuan University |
| authorships[1].author.id | https://openalex.org/A5102829080 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5595-6347 |
| authorships[1].author.display_name | Caixia Fu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[1].affiliations[0].raw_affiliation_string | Sichuan University |
| authorships[1].institutions[0].id | https://openalex.org/I24185976 |
| authorships[1].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Sichuan University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Caixia Fu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Sichuan University |
| authorships[2].author.id | https://openalex.org/A5022306124 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Ling Fu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[2].affiliations[0].raw_affiliation_string | Sichuan University |
| authorships[2].institutions[0].id | https://openalex.org/I24185976 |
| authorships[2].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Sichuan University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ling Fu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Sichuan University |
| authorships[3].author.id | https://openalex.org/A5089287882 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Zhiyun Lu |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[3].affiliations[0].raw_affiliation_string | Sichuan University |
| authorships[3].institutions[0].id | https://openalex.org/I24185976 |
| authorships[3].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Sichuan University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zhiyun Lu |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Sichuan University |
| authorships[4].author.id | https://openalex.org/A5037593812 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-5519-4258 |
| authorships[4].author.display_name | Xuemei Pu |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I24185976 |
| authorships[4].affiliations[0].raw_affiliation_string | Sichuan University |
| authorships[4].institutions[0].id | https://openalex.org/I24185976 |
| authorships[4].institutions[0].ror | https://ror.org/011ashp19 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I24185976 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Sichuan University |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Xuemei Pu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Sichuan University |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60fe8efb393cc9f6374efc64/original/data-driven-machine-learning-models-for-the-quick-and-accurate-prediction-of-tg-and-td-of-oled-materials.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Data-driven machine learning models for the quick and accurate prediction of Tg and Td of OLED materials |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10611 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9900000095367432 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Organic Light-Emitting Diodes Research |
| related_works | https://openalex.org/W1123907903, https://openalex.org/W2188292807, https://openalex.org/W2288334746, https://openalex.org/W2378970241, https://openalex.org/W2055230595, https://openalex.org/W2167605257, https://openalex.org/W4250248449, https://openalex.org/W2381411913, https://openalex.org/W3043432080, https://openalex.org/W4317600379 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.33774/chemrxiv-2021-j5pfd-v2 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60fe8efb393cc9f6374efc64/original/data-driven-machine-learning-models-for-the-quick-and-accurate-prediction-of-tg-and-td-of-oled-materials.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2 |
| primary_location.id | doi:10.33774/chemrxiv-2021-j5pfd-v2 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/60fe8efb393cc9f6374efc64/original/data-driven-machine-learning-models-for-the-quick-and-accurate-prediction-of-tg-and-td-of-oled-materials.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.33774/chemrxiv-2021-j5pfd-v2 |
| publication_date | 2021-07-26 |
| publication_year | 2021 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 6, 46, 58, 86, 213 |
| abstract_inverted_index.In | 171 |
| abstract_inverted_index.K, | 119, 121, 128, 130 |
| abstract_inverted_index.R2 | 116 |
| abstract_inverted_index.Td | 134 |
| abstract_inverted_index.Tg | 125, 220 |
| abstract_inverted_index.We | 207 |
| abstract_inverted_index.an | 182 |
| abstract_inverted_index.at | 205 |
| abstract_inverted_index.by | 65, 74, 150 |
| abstract_inverted_index.in | 223 |
| abstract_inverted_index.is | 53 |
| abstract_inverted_index.it | 52 |
| abstract_inverted_index.of | 9, 16, 39, 89, 112, 142, 167, 178, 228 |
| abstract_inverted_index.on | 219 |
| abstract_inverted_index.to | 56, 173 |
| abstract_inverted_index.we | 68 |
| abstract_inverted_index.Td, | 222 |
| abstract_inverted_index.The | 136 |
| abstract_inverted_index.all | 194 |
| abstract_inverted_index.and | 14, 33, 48, 60, 115, 122, 131, 139, 163, 193, 221 |
| abstract_inverted_index.are | 117, 147, 202 |
| abstract_inverted_index.for | 124, 133, 216 |
| abstract_inverted_index.new | 76 |
| abstract_inverted_index.one | 81 |
| abstract_inverted_index.the | 28, 40, 66, 101, 105, 110, 140, 143, 161, 164, 168, 175, 179, 189, 195, 226, 229 |
| abstract_inverted_index.two | 41 |
| abstract_inverted_index.was | 186 |
| abstract_inverted_index.(Tg) | 32 |
| abstract_inverted_index.0.77 | 123 |
| abstract_inverted_index.0.78 | 132 |
| abstract_inverted_index.MAE, | 113 |
| abstract_inverted_index.OLED | 20, 230 |
| abstract_inverted_index.also | 153 |
| abstract_inverted_index.best | 106 |
| abstract_inverted_index.data | 198 |
| abstract_inverted_index.from | 85 |
| abstract_inverted_index.have | 4 |
| abstract_inverted_index.high | 23, 233 |
| abstract_inverted_index.more | 79 |
| abstract_inverted_index.than | 80 |
| abstract_inverted_index.that | 209 |
| abstract_inverted_index.they | 210 |
| abstract_inverted_index.tool | 215 |
| abstract_inverted_index.turn | 224 |
| abstract_inverted_index.were | 96 |
| abstract_inverted_index.wide | 7, 87 |
| abstract_inverted_index.will | 211 |
| abstract_inverted_index.with | 27, 78, 100, 232 |
| abstract_inverted_index.(Td). | 36 |
| abstract_inverted_index.17.15 | 118 |
| abstract_inverted_index.24.63 | 120 |
| abstract_inverted_index.24.91 | 127 |
| abstract_inverted_index.33.88 | 129 |
| abstract_inverted_index.OLEDs | 17 |
| abstract_inverted_index.RMSE, | 114 |
| abstract_inverted_index.Thus, | 51 |
| abstract_inverted_index.based | 72 |
| abstract_inverted_index.glass | 29 |
| abstract_inverted_index.order | 172 |
| abstract_inverted_index.quick | 59 |
| abstract_inverted_index.range | 8, 88 |
| abstract_inverted_index.tool. | 63 |
| abstract_inverted_index.under | 199 |
| abstract_inverted_index.where | 109 |
| abstract_inverted_index.which | 92, 201 |
| abstract_inverted_index.(OLED) | 2 |
| abstract_inverted_index.Models | 98 |
| abstract_inverted_index.become | 212 |
| abstract_inverted_index.design | 227 |
| abstract_inverted_index.expect | 208 |
| abstract_inverted_index.extend | 174 |
| abstract_inverted_index.freely | 203 |
| abstract_inverted_index.highly | 54 |
| abstract_inverted_index.model. | 170 |
| abstract_inverted_index.models | 95, 146, 192 |
| abstract_inverted_index.online | 183 |
| abstract_inverted_index.study, | 200 |
| abstract_inverted_index.tested | 149 |
| abstract_inverted_index.useful | 214 |
| abstract_inverted_index.values | 111 |
| abstract_inverted_index.Organic | 0 |
| abstract_inverted_index.dataset | 77 |
| abstract_inverted_index.desired | 55 |
| abstract_inverted_index.develop | 57 |
| abstract_inverted_index.exhibit | 104 |
| abstract_inverted_index.further | 12, 148, 159 |
| abstract_inverted_index.involve | 45 |
| abstract_inverted_index.machine | 70, 144 |
| abstract_inverted_index.models, | 181 |
| abstract_inverted_index.optimal | 190 |
| abstract_inverted_index.samples | 83 |
| abstract_inverted_index.thermal | 24, 196 |
| abstract_inverted_index.throngh | 91 |
| abstract_inverted_index.trained | 99 |
| abstract_inverted_index.However, | 11 |
| abstract_inverted_index.LightGBM | 102 |
| abstract_inverted_index.ML-based | 169, 180 |
| abstract_inverted_index.accurate | 61 |
| abstract_inverted_index.dataset, | 152 |
| abstract_inverted_index.ensemble | 93 |
| abstract_inverted_index.explored | 69 |
| abstract_inverted_index.learning | 71, 94, 145 |
| abstract_inverted_index.platform | 185 |
| abstract_inverted_index.practice | 176 |
| abstract_inverted_index.process. | 50 |
| abstract_inverted_index.requires | 18 |
| abstract_inverted_index.results. | 156 |
| abstract_inverted_index.thousand | 82 |
| abstract_inverted_index.Motivated | 64 |
| abstract_inverted_index.algorithm | 103 |
| abstract_inverted_index.available | 204 |
| abstract_inverted_index.collected | 84 |
| abstract_inverted_index.exhibited | 5 |
| abstract_inverted_index.explored. | 97 |
| abstract_inverted_index.framework | 73 |
| abstract_inverted_index.important | 42 |
| abstract_inverted_index.including | 22, 188 |
| abstract_inverted_index.laborious | 49 |
| abstract_inverted_index.materials | 3, 231 |
| abstract_inverted_index.potential | 166 |
| abstract_inverted_index.practical | 165 |
| abstract_inverted_index.predition | 191 |
| abstract_inverted_index.stability | 25, 197 |
| abstract_inverted_index.associated | 26 |
| abstract_inverted_index.changelle, | 67 |
| abstract_inverted_index.exhibiting | 154 |
| abstract_inverted_index.genernally | 44 |
| abstract_inverted_index.materials, | 21 |
| abstract_inverted_index.prediction | 62, 107, 137, 184 |
| abstract_inverted_index.properties | 43 |
| abstract_inverted_index.transition | 30 |
| abstract_inverted_index.application | 177 |
| abstract_inverted_index.development | 13 |
| abstract_inverted_index.performance | 138 |
| abstract_inverted_index.prediction, | 126 |
| abstract_inverted_index.prediction. | 135 |
| abstract_inverted_index.reliability | 162 |
| abstract_inverted_index.temperature | 31, 35 |
| abstract_inverted_index.Experimental | 37, 157 |
| abstract_inverted_index.accelerating | 225 |
| abstract_inverted_index.constructed, | 187 |
| abstract_inverted_index.constructing | 75 |
| abstract_inverted_index.demonstrates | 160 |
| abstract_inverted_index.experimental | 217 |
| abstract_inverted_index.performance, | 108 |
| abstract_inverted_index.performance. | 234 |
| abstract_inverted_index.satisfactory | 155 |
| abstract_inverted_index.verification | 158 |
| abstract_inverted_index.applications. | 10 |
| abstract_inverted_index.decomposition | 34 |
| abstract_inverted_index.literaturesm, | 90 |
| abstract_inverted_index.out-of-sample | 151 |
| abstract_inverted_index.determinations | 38 |
| abstract_inverted_index.generalization | 141 |
| abstract_inverted_index.higher-quality | 19 |
| abstract_inverted_index.investigations | 218 |
| abstract_inverted_index.time-consuming | 47 |
| abstract_inverted_index.commercialization | 15 |
| abstract_inverted_index.light-emitting-diode | 1 |
| abstract_inverted_index.http://oledtppxmpugroup.com. | 206 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.06834021 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |