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.

在化学系化学催化（CAT）和化学结构、动力学和机理-B（CSDM-B）项目的支持下，加州大学洛杉矶分校的Chong Liu和Quanquan Gu以及Jenny Y.加州大学欧文分校的杨正致力于建立一个电化学研究自动化平台，需要最少的人为干预。该项目的成功完成将展示自主电化学研究的可行性，注意事项和力量，并承诺如何进行电化学和电催化的科学研究调查的范式转变。该项目还引入了在跨学科研究环境中培训具有不同技能的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向公众开放，并纳入以电化学为重点的教育靴子营地。将为本科生、研究生和博士后参与者，特别是来自社会经济代表性不足群体的参与者，建立一个关于电化学、自动化和人工智能的靴子训练营。这个靴子训练营将吸引高水平的本科生和研究生，以及博士后学者，将促进跨学科，并将有助于建立一个精通人工智能的化学劳动力。在这个奖项下，加州大学，洛杉矶的Chong Liu和Quanquan Gu以及Jenny Y.加州大学欧文分校的Yang博士正在建立一个概念验证平台，以高通量自主进行电化学研究，并至少部分补充（如果不是取代）手动过程。该团队将开发基于深度学习的算法，以自动分析电化学数据，并为电化学中质子耦合电子转移的机理研究构建实验平台。具体而言，该提案的目的是：（1）开发基于深度学习的自动算法，自动分析作为电化学数据经典示例的循环伏安图;（2）构建一个自主实验平台，该平台可以自动进行电化学测试，并根据团队对深度学习算法和贝叶斯优化的理解迭代设计新的实验，以及（3）利用已建立的平台进行电化学中质子耦合电子转移（PCET）的机理研究，并发现CO2固定的均相电催化中的新反应性。该奖项反映了NSF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。