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教师提供了购买研究活动的支持，以进一步提高其研究能力和有效性，并增强HBCUS的STEM研究和本科教育。杰克逊州立大学的奖项有可能改善生物成像，培训机器学习和人工智能的本科生以及STEM课程的先进技术的生产教育模块。该项目旨在利用机器学习的新颖应用来辨别光发射器中什么原子主导其化学品效率，以提高生物成像设备。该项目的重要性在于促进热解的机器学习知识，并有助于合成新一代的光发射器，这些发射器可以提高生物发光成像的图像质量，并改善了肿瘤治疗的治疗结果。学生参与者将获得关键劳动力技能的培训。该项目旨在使用贝叶斯支撑矢量机（BSVM）学习模型来揭示合成光发射器中高化学兴奋效率的起源。该项目是新颖的，因为它（1）指数通过优化参考轨迹周围的CI海洋获得的临床交叉点（CI）拓扑来刺激轨迹； 2）通过BSVM分隔段在任何瞬间投射轨迹的能量状态； 3）搜索最具影响力的特征，这些特征描述了决策边界并从统计上对表面跳跃轨迹进行了分类； 4）表征与分离质最远的特征，并以很高的概率预测能量状态； 5）建立初始相空间坐标与过渡阈值之间的关系，以促进具有高照明效率的新光发射器的设计过程； 6）开发了多个小型计算模块，每个模块将用作HBCU本科生研究的研究主题。该奖项反映了NSF的法定任务，并通过评估智力优点和更广泛的影响审查标准，通过评估来诚实地获得支持。