Summary of Self-consistent Validation For Machine Learning Electronic Structure, by Gengyuan Hu et al.
Self-consistent Validation for Machine Learning Electronic Structure
by Gengyuan Hu, Gengchen Wei, Zekun Lou, Philip H.S. Torr, Wanli Ouyang, Han-sen Zhong, Chen Lin
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a technique that combines machine learning with self-consistent field methods to improve the accuracy and interpretability of electronic structure predictions. The approach aims to address the issue of generalizability, where machine learning models struggle to perform well on unseen data. By integrating these two approaches, the method achieves both low validation costs and high interpretability, enabling active learning and real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a new way to make computers better at predicting how atoms behave in different situations. Right now, these predictions aren’t very reliable when they’re tested with data that’s never been seen before. To fix this problem, the authors came up with a technique that combines two existing methods. This new approach makes it possible to test and understand how well the computer is doing its job, which is important for using these predictions in real-life situations. |
Keywords
* Artificial intelligence * Active learning * Machine learning
