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.

实验的人工智能（AI）指导设计被毒化以转化化学催化。该项目没有使用以昂贵且困难的实验为特征的催化剂的传统结构和电子特征，而是利用大语模型（LLMS）的最新进展来代表催化剂，以合成程序和反应条件的文本来表示催化剂。该方法具有加速地球含量，活性和选择性催化剂的可能性，从而引起新兴的碳化学工业，从而从二氧化碳（CO2）生产低成本产品。该项目的更广泛影响将有两个目的：（1）教育和激动人心的学生有关LLMS和AI的材料发现； （2）证明基于语言的表示是通用的，并且可以应用于用语言表达的任何过程。LLM方法论可以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开发指导实验空间大小和组成的假设。该项目最初着重于理解和开发用于反向水电转移（RWGS）反应的三金属催化剂。三金属催化剂比双金属催化剂更难表征，这使得它们非常适合不需要催化剂结构具有预测性的方法。通过用语言代表催化剂，物理化学细节（例如由于反应过程中催化剂恢复的催化剂）被基于文本的表示中的实验条件捕获。除了使用语言来识别RWGS反应的新型催化剂的核心方法外，该项目还将评估实验性伪像和不可重现结果对模型性能的影响。基于语言的工作流程将与现有的计算方法集成，以从实验程序的文本中提取机械信息。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为通过评估被认为是宝贵的支持。