在大型语言模型时代,重新思考化学研究
Robert MacKnight1, Daniil A Boiko1, Jose Emilio Regio2
1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Nature computational science
|June 24, 2025
概括
大型语言模型 (LLM) 可以通过帮助规划和分析来推进化学研究. 为了有效使用,必须解决绩效评估中的挑战和诸如偏见之类的伦理考虑.
科学领域:
- 计算化学的计算化学
- 科学中的人工智能.
背景情况:
- 大型语言模型 (LLM) 为增强化学研究的各个方面提供了重大机会.
- 这些包括实验设计,流程优化,数据解释,实验室自动化和科学知识管理.
研究的目的:
- 讨论目前和未来的化学研究领域内LLMs的整合.
- 突出现有的挑战和与在科学背景下部署LLM相关的伦理考虑.
主要方法:
- 对化学研究中LLMs正在进行和潜在应用的审查.
- 讨论绩效评估指标和伦理问题.
主要成果:
- 当在与工具和数据交互的活跃环境中部署时,LLM可以增强化学研究能力.
- 关键的挑战包括强大的绩效评估,确保可重复性,维护数据隐私和减轻算法偏差.
结论:
- 有效整合LLM作为积极的科学合作伙伴需要解决绩效和道德挑战.
- 需要进一步的研究来指导在化学中负责任和有影响力的LLMs的使用.
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