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.

Catalyst项目为传统黑人学院和大学（HBCU）提供支持，以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该教员的研究能力，提高该机构的研究和教学水平。让本科生参与研究体验。杰克逊州立大学的项目应用人工智能（AI）的子集机器学习方法，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。该项目还设计了贝叶斯支持向量机和轨迹表面跳跃模拟的令人兴奋的教育界面，为来自历史上在科学、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。本项目将贝叶斯支持向量机与轨迹表面跳跃方法相结合，探索化学发光产率的起源，并开发用于本科生研究和实验室训练的教学模块。所提出的仿真和数据分析包括：1)能够利用基于贝叶斯支持向量机的分离矩阵对大量的表面跳跃轨迹数据进行处理，提取出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过渡阈值；3)表明轨迹的初始条件如何区分能量状态；4)建立初始核相空间坐标与类概率的关系。它还将复杂的计算协议缩减为多个模块，每个模块的开发都被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，该研究还为社会带来了重大利益，因为成功的结果可能导致生物发光成像技术（BIT）和光动力治疗（PDT）的革命，这将大大提高肿瘤诊断的准确性（通过BIT）和光动力治疗（通过PDT）的生存率。该奖项反映了美国国家科学基金会的法定使命，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。