Kinetic analysis guided by artificial intelligence

人工智能引导的动力学分析

• 批准号: 2904763
• 金额: --
• 依托单位: University of Manchester
• 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-2904763
• 关键词: Kinetic; analysis; guided; artificial; intelligence

Artificial intelligence is revolutionizing all aspects of society, including the field of science, and chemistry is no exception. AI has already been used to solve significant challenges in chemistry, such as designing synthetic routes[1, 2] and predicting the 3D structures of proteins from their amino acid sequences[3, 4]. Additionally, AI has made impressive strides in optimizing reactions[5] and solving the electronic Schrodinger equation[6]. However, there is still significant potential for AI to be applied in the field of catalysis.In this project, we will examine the potential of AI models to revolutionize the design and execution of kinetic analysis for understanding reaction mechanisms. Building upon our recent Nature publication in the application of machine learning for the elucidation of reaction mechanisms in unimolecular reactions,[7] we are uniquely positioned to advance this exciting field.The project will involve a combination of traditional wet-lab techniques to screen and develop catalytic reactions, as well as modern reaction monitoring tools to collect data, and AI-assisted analysis of this data. We will investigate a variety of modern catalytic systems with applications in areas such as drug synthesis, advanced materials, and sustainability.[1] Nature 2020 83; [2] Nature 2018 604; [3] Nature 2021 583; [4] Nature 2021 590; [5] Nature 2021 89; [6] Nat. Chem. 2020 891; [7] Nature 2023 689.

人工智能正在彻底改变社会的各个方面，包括科学领域，化学也不例外。 AI 已被用于解决化学领域的重大挑战，例如设计合成路线 [1, 2] 和根据氨基酸序列预测蛋白质的 3D 结构 [3, 4]。此外，人工智能在优化反应[5]和求解电子薛定谔方程[6]方面取得了令人印象深刻的进步。然而，人工智能在催化领域的应用仍然具有巨大的潜力。在这个项目中，我们将研究人工智能模型彻底改变动力学分析的设计和执行以了解反应机制的潜力。基于我们最近在《自然》杂志上发表的应用机器学习来阐明单分子反应的反应机制，[7]，我们处于独特的地位，可以推进这一令人兴奋的领域。该项目将结合传统的湿实验室技术来筛选和开发催化反应以及现代反应监测工具来收集数据，并对这些数据进行人工智能辅助分析。我们将研究各种现代催化系统在药物合成、先进材料和可持续性等领域的应用。[1]自然 2020 83； [2]《自然》2018年604期； [3]自然2021 583； [4] 自然 2021 590； [5]《自然》2021年89期； [6] 纳特。化学。 2020 891； [7]自然2023 689。