Data Analysis of Cross-Coupling Reactions Using Enhanced Mathematical Methods to Decipher Complexity

使用增强数学方法分析交叉偶联反应的复杂性

• 批准号: 2885384
• 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: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2885384
• 关键词: Data; Analysis; Cross; Coupling; Reactions

Vision: This PhD project is part of a strategically developed mini-Centre for Doctoral Training (CDT) in automating laboratory experiments, known as ALBERT, which is running as a pilot programme at the University of York between 2023-2024. The principal motivation for the ALBERT mini-CDT is to develop the science, engineering, and socio-technology that underpins the building of a laboratory-based robotic system for use in applied experiments across the physical sciences. Automated laboratory experiments are revolutionizing the way that we conduct our science, from a productivity, performance and efficiency perspective. Creating a Chemistry-based ecosystem that is cleaner, greener, safer, and cheaper than anything achievable by current conventional techniques and technologies, is a key driver for this research.Background: Research groups around the World are embedding robotic technologies and data analysis tools into their workflows to accelerate chemical reaction development and understanding, while improving productivity, cost-effectiveness, greenness and cleanliness. In our laboratory we have available to us two Chemspeed robotic systems for accelerating high throughput experimentation (HTE), which produces high volumes of chemical reaction data. This data has a richness to it, particularly relating to the outcomes of widely employed transition metal-catalysed cross-coupling reactions.In a study examining the reaction outcomes from a pharmaceutically relevant catalytic C-H bond functionalisation reaction we have demonstrated the value of analysing the reaction outcomes of a HTE campaign through the utilisation of mathematical methods such as principal component analysis, linear regression and clustering analysis tools. This work ultimately depends on having mathematical expertise in place to examine our reaction data in a rigorous, while ensuring appropriate controls are in place. High-profile research highlights the importance of having mathematical expertise as a cornerstone in the data analysis of chemical reactions, particularly using machine learning methods. The PhD project will thus be carried out by a graduate student with a mathematics undergraduate degree, with interdisciplinary interests.PhD project objectives:Manually assess HTE reaction data from topical synthetic cross-coupling reactions, draw trends and gain broader understanding.Automate data analysis of small (50-300) and large reaction (up to 5000) datasets.Development of a self-optimisation algorithm that can be implemented within the Chemspeed robotic working systems.Assess the impact of automated reaction optimisation routines, comparing against traditional means of working (pros and cons). The PhD student will work on data analysis, script writing and coding. The project is led by Prof. Ian Fairlamb. Several key co-supervisors are in place to play different roles in supporting this interdisciplinary project {Jessica Hargreaves (Maths), Darren Reed (Sociology) and Charlotte Willans (Chemistry)}. There will be collaborative aspects with other Fairlamb research group members and the wider ALBERT CDT programme, as it develops, particularly in its second year 2024/25. Key areas: Synthetic Chemistry, Digital Chemistry, Robotics, Mathematics

愿景：这个博士项目是一个战略性开发的小型博士培训中心（CDT）的一部分，该中心在自动化实验室实验中被称为ALBERT，该中心将于2023-2024年间在约克大学作为试点项目运行。ALBERT mini-CDT的主要动机是开发科学，工程和社会技术，以支持建立基于实验室的机器人系统，用于物理科学的应用实验。从生产力、性能和效率的角度来看，自动化实验室实验正在彻底改变我们进行科学研究的方式。创造一个比现有传统技术更清洁、更环保、更安全、更便宜的化学生态系统是这项研究的关键驱动力。背景：世界各地的研究小组正在将机器人技术和数据分析工具嵌入他们的工作流程中，以加速化学反应的开发和理解，同时提高生产率、成本效益、绿色和清洁度。在我们的实验室中，我们有两个Chemspeed机器人系统可用于加速高通量实验（HTE），从而产生大量的化学反应数据。这些数据非常丰富，特别是与广泛使用的过渡金属催化的交叉偶联反应的结果有关。在一项研究中，我们研究了药物相关的催化C-H键官能化反应的反应结果，我们证明了通过利用数学方法分析HTE活动的反应结果的价值，例如主成分分析，线性回归和聚类分析工具。这项工作最终取决于是否有数学专业知识来严格检查我们的反应数据，同时确保适当的控制到位。备受瞩目的研究强调了数学专业知识作为化学反应数据分析基石的重要性，特别是使用机器学习方法。因此，博士项目将由具有数学本科学位的研究生进行，具有跨学科的兴趣。博士项目目标：手动评估局部合成交叉偶联反应的HTE反应数据，绘制趋势并获得更广泛的理解。自动化小型（50-300）和大型反应（高达5000）数据集的数据分析。开发可在Chemspeed机器人工作系统中实施的自优化算法。评估自动化反应优化例程的影响，与传统的工作方式进行比较（利弊）。博士生将从事数据分析，脚本编写和编码工作。该项目由Ian Fairlamb教授领导。几个关键的共同监督人在支持这个跨学科项目中扮演不同的角色{杰西卡·哈格里夫斯（数学），达伦·里德（社会学）和夏洛特威兰斯（化学）}。随着它的发展，特别是在其第二年2024/25，将与其他Fairlamb研究小组成员和更广泛的ALBERT CDT计划进行合作。关键领域：合成化学、数字化学、机器人技术、数学