Multi-Step Learning and Optimisation

Lead Research Organisation: University of Cambridge
Department Name: Chemistry

Abstract

Year 1: Generic training activities for all first-year student members of the CDT.

Year 2-4: The quest for effective methods of exploring chemical search spaces has ventured from purely in-silico techniques to cyber-physical ones, guiding either robotic or human experimentalists. This blending of AI with physical systems is becoming a well-established technique, and with the addition of high-throughput chemistry techniques, offers a means to efficiently achieve effective and efficient exploration, potentially exceeding human guided techniques. These methods may also be adapted to become a technique for optimising chemical synthesis problems, targeting certain characteristics and molecular traits. Closed-loop systems have already been developed to design efficient production of target molecules and have found a great deal of success. Some of the difficulties with these techniques come about due to the multiple stages involved in complex chemical synthesis tasks and the plethora of objective functions that must be balanced.

Multi-step modelling and design involves multiple steps forming a chain of models, often appearing in situations where multiple chemical products must be produced, and subsequently reacted, to form a final product(s). Although found extensively in chemical process synthesis and synthetic chemistry, many other similar (and dissimilar) contexts exist. Many of the current methods for multistage modelling rely heavily on expert judgement and guesswork to balance objectives, both inter and intra-stage. This represents an obvious weak link in process synthesis. The removal of this form of guesswork is likely to lead to significant improvement in many measures of process success and a speed-up of development.

Planned Impact

Who might benefit from this research? How might they benefit from this research?

Students
(a) The major beneficiaries of the CDT will, of course, be the students that train on the program. They will be equipped with a set of skills that will be highly desirable in the organic molecule making industries. Although the proposal is directing towards a need in the pharmaceutical industry, the training and research skills are totally transferable to industries like the argochemical sector (this is an almost seamless transition as the nature of the needs are near identical to that of pharma) but also the fine chemicals industries, CRO's who serve all of these industries. With some adaptation of the skills accrued then the students will also be able to apply their knowledge to problems in the materials industries, like polymers, organic electronics and chemical biology.

(b) Synthesis will also be evolving in academia and students equipped with skills in digital molecular technologies will be at a significant advantage in being apply to implement the skills acquired while training on the CDT. These students could be the rising stars of academia in 10 years time.

(c) The non-research based training will benefit the students by providing a set of transferable skills that will see them thrive in any chosen career.

(d) The industry contacts that will be generated from the variety of interactions planned in the CDT will give students both experience and insight into the machinations of the industrial sector, helping them to gain a different training experience (form industry taught courses) and hands on experience in industrial laboratories.

(e) All student in UCAM will be able to benefit in some way form the CDT. Training courses will not be restricted to CDT students (only courses that require payment will be CDT only, and even then, we will endeavour to make additional places available for non-CDT students). The overall standard of training for all students wil be raised by a CDT, meaning that benefit will be realised across the students of the associated departments. In additional, non CDT students can also be inspired by the research of the CDT and can immerse new techniques into their own groups.

Academic researchers in related fields (PIs)
(a) new research knowledge that results from this program will benefit PIs in UCAM and across the academic community. All research will be pre-competitive, with any commercial interests managed by Cambridge Enterprise

(b) a change in mnidset of how synthetic research is carried out

(c) new collaborations will be generated withing UCAM, but also externally on a national and international level.

(d) better, more closely aligned, interactions with industry as a result of knowledge transfer

(e) access to outstanding students

Broader public
(a) in principle, more potential medicines could be made available by the research of this CDT.

Economy
(a) a new highly skilled workforce literate in disciplines essential to industry needs will be available
(b) higher productivity in industry, faster access to new medicines
(c) spin out opportunities will create jobs and will stimulate the economy
(d) automation will not remove the need for skilled people, it will allow the researchers to think of solutions to the problems we dont yet understand leading to us being able to discover solutions faster

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024220/1 01/06/2019 30/11/2027
2276523 Studentship EP/S024220/1 01/10/2019 31/12/2022 Rory Geeson
 
Description Early stages of the award demonstrated the application of semantically enriched knowledge management to combustion data management and curation. This presents a route towards a shift in the workflow of combustion researchers towards more actively curated stores of data and automated screening of experimental results with existing data.
Following on from this strand, this award has identified a more generic formulation for many real-world problems (multi-step problems) and has begun to explore new solution techniques.
Exploitation Route The early stage, knowledge graph based outcomes are already being taken forward within the World Avatar project. Later stage outcomes present many opportunities for exploration of further solution techniques and applications to alternative fields and topics.
Sectors Chemicals,Manufacturing, including Industrial Biotechology,Pharmaceuticals and Medical Biotechnology