Summary of Latent Ewald Summation For Machine Learning Of Long-range Interactions, by Bingqing Cheng
Latent Ewald summation for machine learning of long-range interactions
by Bingqing Cheng
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Materials Science (cond-mat.mtrl-sci); 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 The proposed method introduces a straightforward and efficient approach to account for long-range interactions in machine learning interatomic potentials (MLIPs). By learning a latent variable from local atomic descriptors and applying an Ewald summation, this method can effectively eliminate unphysical predictions that arise from neglecting these interactions. The approach is demonstrated on various systems, including charged and polar molecular dimers, bulk water, and the water-vapor interface. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The new method helps machine learning models better understand how molecules interact with each other. It does this by learning a hidden code from local information about atoms and then applying a special sum to capture long-range forces. This improves predictions in complex systems like water and polar molecules, reducing unrealistic results and making the calculations only slightly more time-consuming. |
Keywords
» Artificial intelligence » Machine learning
