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的合作团队加州大学欧文分校的 Y. Yang 正在致力于建立一个需要最少人工干预的电化学研究自动化平台。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，并有望改变电化学和电催化科学研究的开展方式。该项目还提供了在跨学科研究环境中培训具有多种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，并纳入专注于电化学的教育训练营中。将为本科生、研究生和博士后参与者，特别是来自社会经济弱势群体的参与者建立电化学、自动化和人工智能训练营。该训练营将吸引高年级本科生、研究生以及博士后学者，将促进跨学科合作，并有助于建立一支精通人工智能的化学劳动力队伍。在该奖项下，该大学的刘冲和谷泉泉三方合作加州大学洛杉矶分校的 Jenny Y. Yang 和加州大学欧文分校的 Jenny Y. Yang 正在建立一个概念验证平台，以自主进行高通量电化学研究，并至少部分补充（如果不是取代）手册 过程。该团队将开发基于深度学习的算法来自动分析电化学数据，并构建用于电化学中质子耦合电子转移机理研究的实验平台。具体来说，该提案的目标是：（1）开发基于深度学习的自动算法，自动分析循环伏安图作为电化学数据的经典示例； （2）基于团队对深度学习算法和贝叶斯优化的理解，构建自动化电化学测试并迭代设计新实验的自主实验平台；（3）利用已建立的平台进行质子耦合电子的机理研究电化学转移（PCET），并发现二氧化碳固定均相电催化的新反应性。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。