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

在NSF数学和物理科学理事会、人工智能项目和化学部的支持下，加州大学洛杉矶分校（UCLA）的Chong Liu（PI）和Quanquan Gu（co-PI）将应用人工智能（AI）从循环伏安法实验中提取机理信息。电化学测量是分析有机合成化学和酶催化转化反应机理的基本技术。例如，目前手动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AI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械信息。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。该计划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。刘博士和顾博士结合他们在化学和计算机科学方面的专业知识，旨在开发基于人工智能和机器学习的分析程序，以满足循环伏安法机械分析中人工检测的需求。该团队正在努力建立基于卷积神经网络的算法，用于循环伏安图的机制分类。 然后，UCLA团队将测试已建立的算法的可预测性，用于经典手动分析表现不佳的竞争机制，并开发基于贝叶斯优化的其他算法，当可用信息不足以进行结论性分类时，这些算法建议实验测试条件。开发的算法有可能更敏感，并允许访问无偏见的，基于概率的结论，这可能是不可能与人工检查。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.