A Review of Large Language Models and Autonomous Agents in Chemistry
Abstract
Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities in these domains and their potential to accelerate scientific discovery through automation. We also review LLM-based autonomous agents: LLMs with a broader set of tools to interact with their surrounding environment. These agents perform diverse tasks such as paper scraping, interfacing with automated laboratories, and synthesis planning. As agents are an emerging topic, we extend the scope of our review of agents beyond chemistry and discuss across any scientific domains. This review covers the recent history, current capabilities, and design of LLMs and autonomous agents, addressing specific challenges, opportunities, and future directions in chemistry. Key challenges include data quality and integration, model interpretability, and the need for standard benchmarks, while future directions point towards more sophisticated multi-modal agents and enhanced collaboration between agents and experimental methods. Due to the quick pace of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science.
AI-Generated Overview
Overview of the Text "A Review of Large Language Models and Autonomous Agents in Chemistry"
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Research Focus: The paper reviews the impact of large language models (LLMs) and autonomous agents on various tasks in chemistry, including molecule design, property prediction, and synthesis optimization.
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Methodology: The authors conducted a comprehensive review of existing literature on LLMs and autonomous agents, assessing their capabilities, challenges, and future directions in the field of chemistry.
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Results: The review highlights key applications of LLMs, such as improved property prediction, synthesis prediction, molecular generation, and the role of LLM-based autonomous agents in automating tasks like literature scraping and experimental planning.
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Key Contribution(s): The paper extends the current understanding of LLMs by providing a structured categorization of their applications in chemistry, discussing challenges such as data quality, model interpretability, and the integration of LLMs into autonomous systems.
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Significance: The findings suggest that integrating LLMs into chemistry can significantly enhance research efficiency and facilitate scientific discovery by automating complex tasks and improving predictive models.
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Broader Applications: The insights gained from this review are applicable not only to chemistry but can also extend to other scientific domains where LLMs and AI-driven automation could lead to accelerated knowledge discovery and improved research methodologies.