A Review of Large Language Models and Autonomous Agents in Chemistry

• URL: http://arxiv.org/abs/2407.01603v2
• Date: Thu, 25 Jul 2024 21:23:15 GMT
• Title: A Review of Large Language Models and Autonomous Agents in Chemistry
• Authors: Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White,
• Abstract summary: Large language models (LLMs) have emerged as powerful tools in chemistry. This review highlights LLM capabilities in chemistry and their potential to accelerate scientific discovery through automation. As agents are an emerging topic, we extend the scope of our review of agents beyond chemistry.
• Score: 0.7184549921674758
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models [62.37850540570268]
  Existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals. ChemEval identifies 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks. Results show that while general LLMs excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge.
  arXiv  Detail & Related papers  (2024-09-21T02:50:43Z)
• Large Language Model-Based Agents for Software Engineering: A Survey [20.258244647363544]
  The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. We collect 106 papers and categorize them from two perspectives, i.e., the SE and agent perspectives. In addition, we discuss open challenges and future directions in this critical domain.
  arXiv  Detail & Related papers  (2024-09-04T15:59:41Z)
• CACTUS: Chemistry Agent Connecting Tool-Usage to Science [6.832077276041703]
  Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools. We introduce CACTUS, an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of thousands of chemistry questions.
  arXiv  Detail & Related papers  (2024-05-02T03:20:08Z)
• A Survey on the Memory Mechanism of Large Language Model based Agents [66.4963345269611]
  Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems. The key component to support agent-environment interactions is the memory of the agents.
  arXiv  Detail & Related papers  (2024-04-21T01:49:46Z)
• LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments [35.926581910260076]
  We introduce LLMArena, a framework for evaluating the capabilities of large language models in multi-agent dynamic environments. LLArena employs Trueskill scoring to assess crucial abilities in LLM agents, including spatial reasoning, strategic planning, numerical reasoning, risk assessment, communication, opponent modeling, and team collaboration. We conduct an extensive experiment and human evaluation among different sizes and types of LLMs, showing that LLMs still have a significant journey ahead in their development towards becoming fully autonomous agents.
  arXiv  Detail & Related papers  (2024-02-26T11:31:48Z)
• An Autonomous Large Language Model Agent for Chemical Literature Data Mining [60.85177362167166]
  We introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data.
  arXiv  Detail & Related papers  (2024-02-20T13:21:46Z)
• Large Language Model based Multi-Agents: A Survey of Progress and Challenges [44.92286030322281]
  Large Language Models (LLMs) have achieved remarkable success across a wide array of tasks. Recently, based on the development of using one LLM as a single planning or decision-making agent, LLM-based multi-agent systems have achieved considerable progress in complex problem-solving and world simulation.
  arXiv  Detail & Related papers  (2024-01-21T23:36:14Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)
• A Survey on Large Language Model based Autonomous Agents [105.2509166861984]
  Large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence. This paper delivers a systematic review of the field of LLM-based autonomous agents from a holistic perspective. We present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering.
  arXiv  Detail & Related papers  (2023-08-22T13:30:37Z)
• Building Cooperative Embodied Agents Modularly with Large Language Models [104.57849816689559]
  We address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments. We harness the commonsense knowledge, reasoning ability, language comprehension, and text generation prowess of LLMs and seamlessly incorporate them into a cognitive-inspired modular framework. Our experiments on C-WAH and TDW-MAT demonstrate that CoELA driven by GPT-4 can surpass strong planning-based methods and exhibit emergent effective communication.
  arXiv  Detail & Related papers  (2023-07-05T17:59:27Z)
• ChemCrow: Augmenting large-language models with chemistry tools [0.9195187117013247]
  Large-language models (LLMs) have shown strong performance in tasks across domains, but struggle with chemistry-related problems. In this study, we introduce ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery, and materials design. Our agent autonomously planned and executed the syntheses of an insect repellent, three organocatalysts, and guided the discovery of a novel chromophore.
  arXiv  Detail & Related papers  (2023-04-11T17:41:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.