Summary of Retrieval-retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge, by Heewoong Noh et al.
Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
by Heewoong Noh, Namkyeong Lee, Gyoung S. Na, Chanyoung Park
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a machine learning model called Retrieval-Retro for inorganic retrosynthesis planning. Unlike organic retrosynthesis, this area has seen limited application of machine learning. The model retrieves precursor information from a knowledge base and uses attention layers to learn novel synthesis recipes more effectively. Additionally, the model considers thermodynamic relationships between target materials and precursors during retrieval. Experimental results demonstrate Retrieval-Retro’s superiority in retrosynthesis planning, particularly in discovering novel synthesis recipes crucial for materials discovery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Inorganic retrosynthesis planning is important in chemical science, but machine learning has been less used in this area compared to organic retrosynthesis planning. The paper proposes a new method called Retrieval-Retro that helps find the best way to make new materials by looking at precursor information and thermodynamic relationships. This method can learn new recipes more easily than before. It’s an important step forward for discovering new materials. |
Keywords
» Artificial intelligence » Attention » Knowledge base » Machine learning
