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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| Summary: | <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> |
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