EAGER: Accelerating decarbonization by representing catalysts with natural language

EAGER：通过用自然语言表示催化剂来加速脱碳

• 批准号: 2345734
• 负责人: Marc Porosoff
• 金额: $ 29.96万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345734&HistoricalAwards=false
• 关键词: EAGER; Accelerating; decarbonization; representing; catalysts

Artificial intelligence (AI)-directed design of experiments is poised to transform chemical catalysis. Instead of using traditional structural and electronic features of catalysts characterized by expensive and difficult experiments, the project leverages recent progress in large language models (LLMs) to represent catalysts by the text of synthesis procedures and reaction conditions. The approach has potential to accelerate discovery of earth-abundant, active, and selective catalysts to bring rise to an emerging carbo-chemical industry that makes low-cost products from carbon dioxide (CO2). The broader impacts of this project will serve two purposes: (1) Educate and excite students about LLMs and AI for materials discovery; and (2) demonstrate that language-based representations are universal and can be applied to any process that is expressed with language.The LLM methodology accelerates catalyst predictions with natural language processing and Bayesian optimization (BO), while leveraging chemical intuition to develop hypotheses that guide the size and composition of the experimental space. The project focuses initially on understanding and developing trimetallic catalysts for the reverse water-gas shift (RWGS) reaction. Trimetallic catalysts are more difficult to characterize than bimetallic catalysts, making them a good fit for an approach that does not need the catalyst structure to be predictive. By representing the catalysts with language, physical-chemical details such as those due to catalyst restructuring during the reaction, are captured instead by the experimental conditions as included in the text-based representation. Beyond the core approach of utilizing language to identify novel catalysts for RWGS reaction, the project will assess the effects of experimental artifacts and irreproducible results on the model’s performance. The language-based workflow will be integrated with existing computational methods to extract mechanistic information from the text of experimental procedures.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能（AI）指导的实验设计有望改变化学催化，该项目不使用昂贵且困难的实验所具有的传统催化剂结构和电子特征，而是利用大型语言模型（LLM）的最新进展来表示催化剂。该方法有可能加速发现地球上丰富的活性选择性催化剂，从而催生新兴的碳化学工业，从二氧化碳 (CO2) 中生产低成本产品。该项目的影响将达到两个目的：(1) 教育并激发学生关于材料发现的法学硕士和人工智能的知识；(2) 证明基于语言的表示是通用的，可以应用于任何用语言表达的过程。法学硕士方法通过自然语言处理和贝叶斯优化 (BO) 加速催化剂预测，同时利用化学直觉来制定指导实验空间的大小和组成的假设。该项目最初侧重于理解和开发用于反向水煤气的三金属催化剂。转移（RWGS）反应比双金属催化剂更难表征，这使得它们非常适合不需要通过语言、物理化学细节来预测催化剂的方法。除了利用语言识别 RWGS 反应的新型催化剂的核心方法之外，该项目还将评估实验工件和不可重复结果的影响。基于语言的工作流程将与现有的计算方法相结合，从实验程序的文本中提取机械信息。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估被认为值得支持。影响审查标准。