Accelerating mechanistic understanding in chemical synthesis: Design of Experiment, kinetic analysis and systems modelling

加速化学合成机理的理解：实验设计、动力学分析和系统建模

• 批准号: 2270639
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2270639
• 关键词: Accelerating; mechanistic; understanding; chemical; synthesis

The project fits squarely into EPSRC remit, aligning with the research areas: Catalysis, Syntheticorganic chemistry, Chemical reaction dynamics and mechanisms and Manufacturing technologiesand Robotics. The latter area is set as "Grow". Our project will employ a highly instrumented roboticplatform, complemented by rich reaction data analysis. Once several reaction datasets have beengenerated we will examine machine learning algorithms to predict reaction outcomes in new systems(e.g. substrates, conditions, catalyst changes). The project has the potential to fall into ArtificialIntelligence Technologies, which EPSRC has indicated as an area of importance. The Fairlambgroup's current grant, using automation and gathering rich mechanistic data, is supported by EPSRC(EP/S009965/1)

该项目正好符合EPSRC的研究范围，符合以下研究领域：催化、合成有机化学、化学反应动力学和机理以及制造技术和机器人学。后一个区域设置为“Growth”。我们的项目将使用一个高度仪表化的机器人平台，并辅之以丰富的反应数据分析。一旦生成了几个反应数据集，我们将检查机器学习算法，以预测新系统中的反应结果(例如底物、条件、催化剂变化)。该项目有可能落入人工智能技术领域，EPSRC已将其列为一个重要领域。Fairlambgroup目前的赠款，使用自动化和收集丰富的机械数据，得到了EPSRC的支持(EP/S009965/1)