Combining Chemical Robotics and Statistical Methods to Discover Complex Functional Products

结合化学机器人技术和统计方法来发现复杂的功能产品

• 批准号: EP/R009902/1
• 负责人: Alexei Lapkin
• 金额: $ 156.41万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR009902%2F1
• 关键词: Combining; Chemical; Robotics; Statistical; Methods

Robotics and statistical machine learning have revolutionised manufacturing of mechanical devices and our ability to deal with large amounts of data in many areas of human activities. Chemical manufacturing remains one area where both robotics and statistics have seen very limited uptake. However, both are the likely solutions to many challenges facing the chemicals manufacturing industries. The challenge of sustainable manufacturing requires a rapid switch to locally available renewable feedstocks, new sources of energy and use of rapidly re-configurable intensive reactor technologies. However, conventional methods of process development are slow: this stems from the inherent complexity of chemical processes, including multiple interactions of many components over a very broad range of length and timescales, from behaviour of single molecules to the behaviour of cubic-meter-scale manufacturing reactors. This is where machine learning algorithms could provide the solution, with the ability to rapidly identify the underlying interactions and to design the most useful experiments to perform. To use such algorithms effectively we require a new type of a chemical experiment - highly automated and 'intelligent', equipped with sensors and the ability to link with mathematics, the data handling and the machine learning. The main advance on knowledge that this proposal will realise, is the translation of discovery of new chemical products to their manufacture. For this in this project the chemical robot will learn to identify key process parameters that will impact on scaling the process and will help to develop a scale-up model of a process. This ability will have a tremendous impact in all sectors of chemical products manufacturing, with the main societal impact of better process safety, guaranteed product quality and reduced impact of manufacturing on climate change through reduced emissions and feedstocks waste.

机器人技术和统计机器学习已经彻底改变了机械设备的制造以及我们处理人类活动许多领域中大量数据的能力。化学制造仍然是机器人和统计学都非常有限的领域。然而，两者都是化学品制造业面临的许多挑战的可能解决方案。可持续制造业的挑战要求迅速转向当地可获得的可再生原料、新能源和使用可快速重新配置的密集反应堆技术。然而，传统的工艺开发方法是缓慢的：这源于化学工艺的固有复杂性，包括许多组分在非常广泛的长度和时间范围内的多重相互作用，从单个分子的行为到100米规模的制造反应器的行为。这就是机器学习算法可以提供解决方案的地方，能够快速识别潜在的相互作用，并设计最有用的实验来执行。为了有效地使用这些算法，我们需要一种新型的化学实验-高度自动化和“智能”，配备传感器以及与数学，数据处理和机器学习相关联的能力。这一建议将实现的主要知识进步是将新化学产品的发现转化为生产。为此，在本项目中，化学机器人将学习识别将影响过程缩放的关键过程参数，并将有助于开发过程的放大模型。这种能力将对化学产品制造的所有部门产生巨大影响，主要的社会影响是更好的工艺安全性，保证产品质量，并通过减少排放和原料浪费来减少制造对气候变化的影响。

项目成果

Summit: Benchmarking Machine Learning Methods for Reaction Optimisation

• DOI: 10.1002/cmtd.202000051
• 发表时间: 2021-02-01
• 期刊: CHEMISTRYMETHODS
• 作者: Felton, Kobi C.;Rittig, Jan G.;Lapkin, Alexei A.
• 通讯作者: Lapkin, Alexei A.

A molecular computing approach to solving optimization problems via programmable microdroplet arrays

• DOI: 10.1016/j.matt.2021.03.002
• 发表时间: 2021-04-07
• 期刊: MATTER
• 影响因子: 18.9
• 作者: Guo, Si Yue;Friederich, Pascal;Aspuru-Guzik, Alan
• 通讯作者: Aspuru-Guzik, Alan

Automated robotic platforms in design and development of formulations

• DOI: 10.1002/aic.17248
• 发表时间: 2021-03-03
• 期刊: AICHE JOURNAL
• 影响因子: 3.7
• 作者: Cao，Liwei;Russo，Danilo;Lapkin，Alexei A.
• 通讯作者: Lapkin，Alexei A.

From Platform to Knowledge Graph: Evolution of Laboratory Automation.

• DOI: 10.1021/jacsau.1c00438
• 发表时间: 2022-02-28
• 期刊: JACS Au
• 影响因子: 8
• 作者: Bai J;Cao L;Mosbach S;Akroyd J;Lapkin AA;Kraft M
• 通讯作者: Kraft M

From Platform to Knowledge Graph: Evolution of Laboratory Automation

从平台到知识图：实验室自动化的演变

• DOI: 10.17863/cam.83288
• 发表时间: 2022
• 作者: Bai J