Summary of Self-consistency Training For Density-functional-theory Hamiltonian Prediction, by He Zhang et al.
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
by He Zhang, Chang Liu, Zun Wang, Xinran Wei, Siyuan Liu, Nanning Zheng, Bin Shao, Tie-Yan Liu
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM)
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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 research proposes self-consistency training for predicting mean-field Hamiltonian matrices in density functional theory, a fundamental problem-solving approach in molecular science. The task is challenging due to insufficient labeled data, but the proposed method does not require labels and can leverage a large amount of unlabeled data. This exact training method enables generalization and addresses data scarcity challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predicting the mean-field Hamiltonian matrix in density functional theory is an important problem-solving approach for molecular science. However, it’s limited by insufficient labeled data. The researchers propose a new training method called self-consistency training that doesn’t need labels. This helps solve the data challenge and improves generalization. |
Keywords
* Artificial intelligence * Generalization
