Summary of A Review Of Large Language Models and Autonomous Agents in Chemistry, by Mayk Caldas Ramos et al.
A Review of Large Language Models and Autonomous Agents in Chemistry
by Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Chemical Physics (physics.chem-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 review highlights the capabilities of large language models (LLMs) in chemistry, including molecule design, property prediction, and synthesis optimization. LLMs have the potential to accelerate scientific discovery through automation. The paper also reviews LLM-based autonomous agents that perform tasks such as paper scraping, interfacing with automated laboratories, and synthesis planning. These agents are an emerging topic beyond chemistry and are being discussed across various scientific domains. The review covers recent history, current capabilities, and design of LLMs and autonomous agents, addressing specific challenges, opportunities, and future directions in chemistry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools that can help scientists design new molecules, predict their properties, and optimize their synthesis. This means they can be used to accelerate scientific discovery and make it easier for scientists to do their jobs. The paper reviews what LLMs have done so far in chemistry and how they might be used in the future. It also talks about autonomous agents that use LLMs to interact with their environment and perform tasks like scraping papers, working with automated labs, and planning syntheses. |
Keywords
* Artificial intelligence * Optimization
