Automatic Prediction and Characterisation of Complex Chemical Reactions

复杂化学反应的自动预测和表征

• 批准号: 2228635
• 金额: --
• 依托单位: University of Warwick
• 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-2228635
• 关键词: Automatic; Prediction; Characterisation; Complex; Chemical

Around 90% of all chemical processes use catalysis to control reactivity and selectivity, yet the design of new catalysts too often depends on informed trial-and-error to make progress. This project will directly address the challenge of catalyst design by developing new simulation tools based on network theory, big data and machine learning. We will develop new computational methods for auto-discovery of chemical reactions, enabling us to build microkinetic models to predict the outcomes of real-world laboratory experiments. This multi-scale approach will provide a new direct route towards catalyst design and optimization.

大约90%的化学过程使用催化来控制反应性和选择性，然而新催化剂的设计往往依赖于知情的试错来取得进展。该项目将通过开发基于网络理论、大数据和机器学习的新型仿真工具，直接解决催化剂设计的挑战。我们将为化学反应的自动发现开发新的计算方法，使我们能够建立微动力学模型来预测现实世界实验室实验的结果。这种多尺度方法将为催化剂的设计和优化提供新的直接途径。