Self-consistent Validation for Machine Learning Electronic Structure
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..
| Medienart: |
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
arXiv.org - (2024) vom: 15. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hu, Gengyuan [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
000 |
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| PPN (Katalog-ID): |
XAR042521521 |
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| 245 | 1 | 0 | |a Self-consistent Validation for Machine Learning Electronic Structure |
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| 520 | |a 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. | ||
| 650 | 4 | |a Computer Science - Machine Learning |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Physics - Chemical Physics |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Physics - Computational Physics |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 000 |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 530 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Wei, Gengchen |4 aut | |
| 700 | 1 | |a Lou, Zekun |4 aut | |
| 700 | 1 | |a Torr, Philip H. S. |4 aut | |
| 700 | 1 | |a Ouyang, Wanli |4 aut | |
| 700 | 1 | |a Zhong, Han-sen |4 aut | |
| 700 | 1 | |a Lin, Chen |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t arXiv.org |g (2024) vom: 15. Feb. |
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