Catalyst: Chemiluminescence, Quantum Yield and High-Dimensional Data Analysis in Trajectory Surface Hopping Simulation

催化剂：弹道表面跳跃模拟中的化学发光、量子产率和高维数据分析

• 批准号: 2100971
• 负责人: Jian-Ge Zhou
• 金额: $ 15万
• 依托单位: Jackson State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100971&HistoricalAwards=false
• 关键词: Catalyst; Chemiluminescence; Quantum; Yield; Dimensional

Catalyst Projects provide support for Historically Black Colleges and Universities (HBCU) to work towards establishing research capacity of faculty to strengthen science, technology, engineering and mathematics undergraduate education and research. It is expected that the award will further the faculty member's research capability, improve research and teaching at the institution. and involve undergraduate students in research experiences. The project at Jackson State University applies machine learning approaches, the subset of artificial intelligence (AI), to automatically collect and analyze the nonadiabatic molecular dynamics data to uncover the mechanism of improving quantum yield. The project also designs the exciting educational interface of Bayesian support vector machines and trajectory surface hopping simulation, which provides unique research experience and practical AI skill for undergraduate students from populations that are historically underrepresented in science, technology, engineering and mathematics (STEM).The project integrates Bayesian support vector machines with trajectory surface hopping approach to explore the origin of chemiluminescence yield, and develops the educational modules for undergraduate research and lab training. The proposed simulation and data analysis includes: 1) being able to process the vast amount of the surface hopping trajectory data via Bayesian support vector machines - based separatrices to extract useful and meaningful information of quantum yield; 2) uncovering the transition threshold through high-weight support vectors separating energy subspaces; 3) manifesting how initial conditions of trajectories differentiate energy states; 4) establishing the relation between initial nuclear phase space coordinates and class probabilities. It also sizes down the sophisticated computational protocol into multiple modules, and each module development is used as a research project for undergraduate participants. Beyond the local and regional educational benefits, the research entails a major benefit to society in that successful results could lead to the revolution of bioluminescence imaging technology (BIT) and photodynamic therapy (PDT), which would greatly enhance the accuracy of tumor diagnosis (via BIT) and the survival rate in cancer treatment (via PDT).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.

催化剂项目为历史悠久的黑人学院和大学 (HBCU) 提供支持，致力于建立教师的研究能力，以加强科学、技术、工程和数学本科教育和研究。预计该奖项将进一步提高教师的研究能力，改善该机构的研究和教学。并让本科生参与研究经历。杰克逊州立大学的该项目应用机器学习方法（人工智能（AI）的子集）来自动收集和分析非绝热分子动力学数据，以揭示提高量子产率的机制。该项目还设计了令人兴奋的贝叶斯支持向量机和轨迹表面跳跃模拟的教育界面，为历史上在科学、技术、工程和数学 (STEM) 领域代表性不足的本科生提供独特的研究经验和实用的人工智能技能。该项目将贝叶斯支持向量机与轨迹表面跳跃方法相结合，探索化学发光产量的起源，并开发用于本科生研究和实验室培训的教育模块。 所提出的模拟和数据分析包括：1）能够通过基于贝叶斯支持向量机的分离来处理大量的表面跳跃轨迹数据，以提取有用且有意义的量子产率信息； 2）通过分离能量子空间的高权重支持向量揭示转换阈值； 3）表明轨迹的初始条件如何区分能态； 4)建立初始核相空间坐标与类概率之间的关系。它还将复杂的计算协议缩小为多个模块，每个模块的开发都用作本科生参与者的研究项目。除了当地和地区的教育效益外，该研究还给社会带来重大利益，因为成功的结果可能会引发生物发光成像技术（BIT）和光动力疗法（PDT）的革命，这将大大提高肿瘤诊断的准确性（通过BIT）和癌症治疗的存活率（通过 PDT）。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和更广泛的影响审查标准进行评估，被认为值得支持。