Automated Electrochemical Research based on Deep Learning

基于深度学习的自动化电化学研究

• 批准号: 2247426
• 负责人: Chong Liu
• 金额: $ 90万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247426&HistoricalAwards=false
• 关键词: Automated; Electrochemical; Research; based; Deep

With support from the Chemical Catalysis (CAT) and Chemical Structure, Dynamics, and Mechanisms-B (CSDM-B) programs in the Division of Chemistry, the collaborative team of Chong Liu and Quanquan Gu of the University of California, Los Angeles and Jenny Y. Yang of the University of California, Irvine is working to establish an electrochemical research automation platform that requires minimal human intervention. Successful completion of this project will showcase the feasibility, caveats, and power of autonomous electrochemical research and promise a paradigm shift in how scientific research investigation in electrochemistry and electrocatalysis will be conducted. The project also introduces the opportunity of training the next-generation researchers and workforce with diverse skill sets in an interdisciplinary research environment. The software and methodology developed will be made publicly accessible free of charge and incorporated into an educational boot camp focused on electrochemistry. A boot camp on electrochemistry, automation, and artificial intelligence for undergraduate, graduate, and postdoctoral participants, particularly those from socio-economically underrepresented groups, will be established. This boot camp will engage senior-level undergraduate and graduate students, as well as postdoctoral scholars, will foster interdisciplinarity and will help to build an AI-savvy chemistry workforce.Under this award, the tripartite collaborative of Chong Liu and Quanquan Gu of the University of California, Los Angeles and Jenny Y. Yang of the University of California, Irvine are establishing a proof-of-concept platform to autonomously conduct electrochemistry research with high throughput and at least partly supplement, if not replace, the manual process. The team will develop algorithms based on deep learning to automatically analyze electrochemical data and construct an experimentation platform for mechanistic studies of proton-coupled electron transfer in electrochemistry. Specifically, the aims of this proposal are: (1) to develop automatic algorithms based on deep learning that automatically analyze cyclic voltammograms as a classic example of electrochemical data; (2) to construct an autonomous experimentation platform that automates electrochemical testing and iteratively designs new experiments based on the group's understanding of the deep-learning algorithm and Bayesian optimization and (3) to employ the established platform to conduct mechanistic studies of proton-coupled electron transfer (PCET) in electrochemistry and discover new reactivities in homogenous electrocatalysis of CO2 fixation.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.

在化学催化（CAT）和化学结构，动力学和机制B（CSDM-B）方案的支持下，Chong Liu的合作团队和加利福尼亚大学，洛杉矶大学，洛杉矶大学和加利福尼亚大学的Jenny Y. Yang的合作团队需要建立一个电解自动化的人为分钟的分钟。该项目的成功完成将展示自动电化学研究的可行性，警告和力量，并有望在如何进行电化学和电催化的科学研究研究中改变范式。该项目还介绍了在跨学科研究环境中以各种技能组合培训下一代研究人员和劳动力的机会。开发的软件和方法将免费公开访问，并将其纳入专注于电化学的教育训练营中。将建立针对本科，研究生和博士后参与者的电化学，自动化和人工智能的新兵训练营，尤其是来自社会经济不足的群体的参与者。这个新兵训练营将吸引高级本科生和研究生以及博士后学者，将培养跨学科性，并将有助于建立一个精通AI的化学劳动力。自主进行电化学研究以高吞吐量和至少部分补充（如果不替换）手动过程。该团队将基于深度学习来开发算法，以自动分析电化学数据并构建一个实验平台，以用于电化学中质子耦合电子传输的机械研究。具体而言，该提案的目的是：（1）基于深度学习的自动算法开发自动算法，该算法自动将环状伏安图作为电化学数据的经典示例； （2）构建一个自主实验平台，该平台可自动化电化学测试并迭代地设计新的实验，该实验基于该小组对深入学习算法和贝叶斯优化的理解，并（3）采用既定平台来在质子偶联电子（PCET）中进行机械转移（PCET）的机械性研究，并在电化学中均具有固定性，并在电化学中发现了电源，并在电化学中逐渐转移。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。