Research Initiation Award: Thermal Decomposition of Four-membered Heterocyclic Peroxides, Data Mining in Nonadiabatic Trajectories, and Chemiexcitation Efficiency

研究启动奖：四元杂环过氧化物的热分解、非绝热轨迹数据挖掘、化学激发效率

• 批准号: 2300321
• 负责人: Jian-Ge Zhou
• 金额: $ 30万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300321&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Thermal; Decomposition

HBCU-UP’s Research Initiation Awards provide support for STEM faculty to pursue research activities to further their research capabilities and effectiveness and enhance STEM research and undergraduate education at HBCUs. This award to Jackson State University has the potential to improve bio-imagery, train undergraduate students in machine learning and artificial intelligence, and produce education modules in advanced technology for STEM courses. The project aims to use a novel application of machine learning to discern what atoms in a light emitter dominate its chemiexcitation efficiency to improve bio-imaging devices. The significance of this projects is in advancing knowledge of machine learning of thermolysis and assisting in the synthesis of a new generation of light emitters that advances image quality for bioluminescent imaging and improved treatment outcomes for tumor therapy. Student participants will receive training in critical workforce skills. This project seeks to use the Bayesian Support Vector Machine (BSVM) learning model to uncover the origins of high chemiexcitation efficiency in synthesized light emitters. The project is novel in that it (1) expounds the excitation of the trajectories via the conical intersection (CI) topology obtained by optimizing the CI seams around the reference trajectories; 2) projects the energy state of a trajectory at any instant via the BSVM separatrix; 3) searches the most influential features that describe the decision boundary and classifies surface hopping trajectories statistically; 4) characterizes the features that are farthest from the separatrix and predicts the energy states with high probability; 5) establishes the relation between initial phase space coordinates and transition thresholds to facilitate the designing process for new light emitters with high lighting efficiency; and 6) develops multiple small computational modules, each of which will be used as research topics for HBCU undergraduate student research.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.

HBCU-UP 的研究启动奖为 STEM 教师开展研究活动提供支持，以提高他们的研究能力和有效性，并加强 HBCU 的 STEM 研究和本科生教育。该奖项授予杰克逊州立大学，有潜力改善生物图像、培养本科生。该项目旨在利用机器学习的新颖应用来识别光发射器中的哪些原子主导其化学激发效率，以改进生物成像设备。该项目的意义在于推进热解机器学习的知识，并协助合成新一代光发射器，从而提高生物发光成像的图像质量并改善肿瘤治疗的治疗效果。学生参与者将接受关键劳动力技能的培训。该项目旨在使用贝叶斯支持向量机（BSVM）学习模型来揭示合成光发射器高化学激发效率的起源，该项目的新颖之处在于（1）通过以下方式阐述了轨迹的激发。通过优化参考轨迹周围的 CI 接缝获得圆锥形相交 (CI) 拓扑；2) 通过 BSVM 分离矩阵投影任意时刻轨迹的能量状态；3) 搜索描述决策边界并对表面跳跃进行分类的最有影响力的特征；紧密的轨迹；4）表征距离分界线最远的特征并以高概率预测能量状态；5）建立初始相空间坐标和转变阈值之间的关系；具有高照明效率的新型发光器的设计过程；6) 开发多个小型计算模块，每个模块都将用作 HBCU 本科生研究的研究课题。该奖项反映了 NSF 的法定使命，并被认为值得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。