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教师提供支持，以开展研究活动，进一步提高他们的研究能力和有效性，并加强HBCU的STEM研究和本科教育。授予杰克逊州立大学的这一奖项有可能改善生物图像，培训机器学习和人工智能方面的本科生，并为STEM课程制作先进技术的教育模块。该项目旨在使用机器学习的一种新应用来识别光发射器中的哪些原子主导其化学激发效率，以改善生物成像设备。该项目的重要性在于推进机器学习的知识，并协助合成新一代光发射器，提高生物发光成像的图像质量，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体中高化学激发效率的起源。该方案的新颖之处在于：（1）通过优化参考轨迹周围的圆锥相交（CI）接缝获得CI拓扑来阐述轨迹的激励;（2）通过BSVM分界线来投影轨迹在任何时刻的能量状态;（3）搜索描述决策边界的最有影响力的特征并对表面跳跃轨迹进行统计分类; 5）建立初始相空间坐标与跃迁阈值之间的关系，以便于设计具有高发光效率的新型发光体; 6）开发多个小型计算模块，每一个都将被用作HBCU本科生研究的研究课题。这个奖项反映了NSF的法定使命，并被认为是值得通过评估使用的支持基金会的学术价值和更广泛的影响审查标准。