EAGER: Accelerating decarbonization by representing catalysts with natural language

人工智能(AI)指导的实验设计有望改变化学催化。该项目利用大型语言模型(LLM)的最新进展，通过合成过程和反应条件的文本来表示催化剂，而不是使用昂贵和困难的实验来表征催化剂的传统结构和电子特征。这种方法有可能加速发现富含地球的、活性的和选择性的催化剂，从而催生一个新兴的碳化工业，从二氧化碳(CO2)中生产低成本的产品。这个项目的更广泛的影响将有两个目的：(1)教育和激发学生关于材料发现的LLM和人工智能；(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。LLM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假设，指导实验空间的大小和组成。该项目最初的重点是了解和开发用于逆水煤气变换(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来预测的方法。通过用语言表示催化剂，诸如在反应过程中由于催化剂重组而引起的物理化学细节被包括在基于文本的表示中的实验条件所捕捉。除了利用语言为RWGS反应寻找新的催化剂这一核心方法之外，该项目还将评估实验人工制品和不可重现的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影响审查标准进行评估，被认为值得支持。

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.