Self-consistent validation for machine learning electronic structure
<p>Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems. Despite its potential, there is less guarantee for the model to generalize to unseen data that hinders its application in real-world scenarios. To address this issue, a technique has...
| Main Authors: | , , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
2024
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| _version_ | 1811140146742951936 |
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| author | Hu, G Wei, G Lou, Z Torr, PHS Ouyang, W Zhong, H-s Lin, C |
| author_facet | Hu, G Wei, G Lou, Z Torr, PHS Ouyang, W Zhong, H-s Lin, C |
| author_sort | Hu, G |
| collection | OXFORD |
| description | <p>Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems. Despite its potential, there is less guarantee for the model to generalize to unseen data that hinders its application in real-world scenarios. To address this issue, a technique has been proposed to estimate the accuracy of the predictions. This method integrates machine learning with self-consistent field methods to achieve both low validation cost and interpret-ability. This, in turn, enables exploration of the model’s ability with active learning and instills confidence in its integration into real-world studies.</p> |
| first_indexed | 2024-09-25T04:17:21Z |
| format | Conference item |
| id | oxford-uuid:80b5ca60-d37c-4248-b96b-ce76f6a46439 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:17:21Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | oxford-uuid:80b5ca60-d37c-4248-b96b-ce76f6a464392024-07-22T07:48:17ZSelf-consistent validation for machine learning electronic structureConference itemhttp://purl.org/coar/resource_type/c_5794uuid:80b5ca60-d37c-4248-b96b-ce76f6a46439EnglishSymplectic Elements2024Hu, GWei, GLou, ZTorr, PHSOuyang, WZhong, H-sLin, C<p>Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems. Despite its potential, there is less guarantee for the model to generalize to unseen data that hinders its application in real-world scenarios. To address this issue, a technique has been proposed to estimate the accuracy of the predictions. This method integrates machine learning with self-consistent field methods to achieve both low validation cost and interpret-ability. This, in turn, enables exploration of the model’s ability with active learning and instills confidence in its integration into real-world studies.</p> |
| spellingShingle | Hu, G Wei, G Lou, Z Torr, PHS Ouyang, W Zhong, H-s Lin, C Self-consistent validation for machine learning electronic structure |
| title | Self-consistent validation for machine learning electronic structure |
| title_full | Self-consistent validation for machine learning electronic structure |
| title_fullStr | Self-consistent validation for machine learning electronic structure |
| title_full_unstemmed | Self-consistent validation for machine learning electronic structure |
| title_short | Self-consistent validation for machine learning electronic structure |
| title_sort | self consistent validation for machine learning electronic structure |
| work_keys_str_mv | AT hug selfconsistentvalidationformachinelearningelectronicstructure AT weig selfconsistentvalidationformachinelearningelectronicstructure AT louz selfconsistentvalidationformachinelearningelectronicstructure AT torrphs selfconsistentvalidationformachinelearningelectronicstructure AT ouyangw selfconsistentvalidationformachinelearningelectronicstructure AT zhonghs selfconsistentvalidationformachinelearningelectronicstructure AT linc selfconsistentvalidationformachinelearningelectronicstructure |