Summary of Response Matching For Generating Materials and Molecules, by Bingqing Cheng
Response Matching for generating materials and molecules
by Bingqing Cheng
First submitted to arxiv on: 15 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci); 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 presents Response Matching (RM), a novel generative method that leverages physical symmetries to generate new molecular and material structures. By matching responses in energy and stress, RM respects permutation, translation, rotation, and periodic invariances, making it the first model to handle both molecules and bulk materials under the same framework. The authors demonstrate RM’s efficiency and generalization across three systems: a small organic molecular dataset, stable crystals from the Materials Project, and one-shot learning on a single diamond configuration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates new materials and molecules using machine learning. It finds a way to make sure the new structures are real by looking at how they change when something happens to them. This is different from other models that just try to copy what already exists. The authors show that their method works well for small molecules, crystals, and even making new materials one time. |
Keywords
» Artificial intelligence » Generalization » Machine learning » One shot » Translation
