EAGER: ADAPT: AI-based Categorization to Decipher Reaction Mechanisms from Cyclic Voltammetry

在美国国家科学基金会数学与物理科学理事会、人工智能项目和化学学部的支持下，加州大学洛杉矶分校（UCLA）的刘冲（PI）和谷泉泉（共同PI）将应用人工智能（AI）从循环伏安实验中提取机制信息。电化学测量是分析合成有机化学和酶催化转化反应机理的一项基本技术。例如，目前手工分析测量结果的做法需要大量的培训，可能会引入无意识的偏见，并且与寻找最佳催化剂的高通量筛选不兼容。该研究项目通过开发基于人工智能的算法来解决上述挑战，该算法可以破译给定反应的实验数据并自动提取机制信息。该项目有可能改变研究人员分析催化剂或反应促进剂性质的方式，并可能加速我们对反应机理的发现。PI和co-PI都努力鼓励不同研究和社会经济背景的学生更广泛地参与。该课程的学生将获得电化学、编程、机器学习和人工智能方面的技能和知识。化学和计算机科学之间的跨学科合作和学生的共同指导将有助于培养下一代精通人工智能的劳动力。结合他们在化学和计算机科学方面的专业知识。Liu和Gu博士的目标是开发一种基于人工智能和机器学习的分析程序，以减轻循环伏安法中机械分析的人工检查需求。该团队正在努力建立基于卷积神经网络的算法，用于从循环伏安图中对机制进行分类。加州大学洛杉矶分校的研究小组随后将测试既定算法的可预测性，以应对传统人工分析表现不佳的竞争机制，并开发基于贝叶斯优化的额外算法，当可用信息不足以进行决定性分类时，该算法可以提出实验测试条件。开发的算法有可能更加敏感，并允许获得人工检查可能无法实现的无偏、基于概率的结论。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

WIth support from the NSF Directorate of Mathematical and Physical Sciences, Artificial Intelligence Program, and the Division of Chemistry, Chong Liu (PI) and Quanquan Gu (co-PI) at the University of California, Los Angeles (UCLA) will apply artificial intelligence (AI) to extract mechanistic information from cyclic voltammetry experiments. Electrochemical measurement is a fundamental technique for the analysis of reaction mechanisms from synthetic organic chemistry and enzyme-catalyzed transformations. The current practice of manual analysis of measurement results demands significant training, may introduce unconscious bias, and is not compatible with high-throughput screening in search of optimal catalysts, for example. This research project addresses the aforementioned challenge by developing AI-based algorithms that decipher experimental data for a given reaction and automatically extract mechanistic information. The project has the potential of transforming how researchers analyze the properties of catalysts or reaction promoters and may accelerate our discovery of reactionmechanism. Both the PI and co-PI strive to encourage a broader participation among students of different research and socio-economic backgrounds. Students in this program will acquire skills and knowledge in electrochemistry, programming, machine-learning and artificial intelligence. The interdisciplinary collaboration and the co-mentoring of students between chemistry and computer science will help to develop the next-generation AI-savvy workforce.Combining their expertise in chemistry and computer science, Drs. Liu and Dr. Gu aim to develop an analytic procedure based on AI and machine learning that alleviates the demand of manual inspection for mechanistic analysis in cyclic voltammetry. The team is working to establish algorithms based upon a convolutional neural network for mechanism categorization from cyclic voltammograms. The UCLA team will then test the predictability of the established algorithms for competing mechanisms for which classic manual analysis has performed poorly, and develop additional algorithms based on Bayesian optimization that suggest experimental testing conditions when available information is not sufficient for a conclusive classification. The developed algorithms have the potential to be more sensitive and allow access to unbiased, probability-based conclusions that may not be possible with manual inspection.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.