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AI3SD video: harnessing advanced algorithms to enable the automated optimisation of telescoped chemical reactions; performance directed self-optimisation of bimetallic nanoparticle catalysts

AI3SD video: harnessing advanced algorithms to enable the automated optimisation of telescoped chemical reactions; performance directed self-optimisation of bimetallic nanoparticle catalysts
AI3SD video: harnessing advanced algorithms to enable the automated optimisation of telescoped chemical reactions; performance directed self-optimisation of bimetallic nanoparticle catalysts
The catalytic performance of nanoparticles is dependent on an extensive number of properties, reactions conditions and combinations thereof; however, very few methods employing multivariate closed-loop optimisation of nanoparticle catalysts have been reported to date. Here we demonstrate a machine learning-driven reactor platform for the performance directed synthesis of nanoparticle catalysts. Our experimental strategy uses an automated two-stage continuous flow reactor with decoupled residence times, allowing the precise synthesis of gold-silver nanoparticles (AuAgNP) with variable metal compositions, and subsequent performance analysis using a 4-nitrophenol reduction reaction. Quantification of the reaction conversion using inline UV-Vis spectroscopy enables the direct observation of the catalyst performance in real time, providing an efficient response for the performance directed synthesis of the most catalytically active nanoparticles. This approach paves the way for the rapid synthesis and optimisation of new nanoparticle catalysts, thereby streamlining the development of sustainable chemical processes. In terms of algorithm development, as many real-world optimisation problems consist of multiple conflicting objectives and constraints which can be composed of both continuous and discrete variables, we are addressing a significant issue. Given the inherent nature of continuous variables, i.e. they have a real value in the desired optimisation range of the variable, they are often easier to explore with a wider array of applicable optimisation techniques. Discrete variables, however, can take the form of integer values or categorical values (materials, reaction solvents). In many cases these optimisations can be expensive to evaluate in terms of time or monetary resources. It is therefore necessary to utilise algorithms that can efficiently guide the search towards the optimum set of conditions for a given problem to reduce costs. He will harness a mixed variable multi-objective Bayesian optimisation algorithm to tackle the problem of simultaneously exploring continuous and discrete variable in the same optimisation, which if successful with represent a step change in this AI field.
Chamberlain, Thomas
d3e126ac-a15e-4724-aaa4-1dc66bf8b352
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Chamberlain, Thomas
d3e126ac-a15e-4724-aaa4-1dc66bf8b352
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Kanza, Samantha
b73bcf34-3ff8-4691-bd09-aa657dcff420
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Chamberlain, Thomas (2022) AI3SD video: harnessing advanced algorithms to enable the automated optimisation of telescoped chemical reactions; performance directed self-optimisation of bimetallic nanoparticle catalysts. Frey, Jeremy G., Kanza, Samantha and Niranjan, Mahesan (eds.) AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom. 01 - 03 Mar 2022. (doi:10.5258/SOTON/AI3SD0212).

Record type: Conference or Workshop Item (Other)

Abstract

The catalytic performance of nanoparticles is dependent on an extensive number of properties, reactions conditions and combinations thereof; however, very few methods employing multivariate closed-loop optimisation of nanoparticle catalysts have been reported to date. Here we demonstrate a machine learning-driven reactor platform for the performance directed synthesis of nanoparticle catalysts. Our experimental strategy uses an automated two-stage continuous flow reactor with decoupled residence times, allowing the precise synthesis of gold-silver nanoparticles (AuAgNP) with variable metal compositions, and subsequent performance analysis using a 4-nitrophenol reduction reaction. Quantification of the reaction conversion using inline UV-Vis spectroscopy enables the direct observation of the catalyst performance in real time, providing an efficient response for the performance directed synthesis of the most catalytically active nanoparticles. This approach paves the way for the rapid synthesis and optimisation of new nanoparticle catalysts, thereby streamlining the development of sustainable chemical processes. In terms of algorithm development, as many real-world optimisation problems consist of multiple conflicting objectives and constraints which can be composed of both continuous and discrete variables, we are addressing a significant issue. Given the inherent nature of continuous variables, i.e. they have a real value in the desired optimisation range of the variable, they are often easier to explore with a wider array of applicable optimisation techniques. Discrete variables, however, can take the form of integer values or categorical values (materials, reaction solvents). In many cases these optimisations can be expensive to evaluate in terms of time or monetary resources. It is therefore necessary to utilise algorithms that can efficiently guide the search towards the optimum set of conditions for a given problem to reduce costs. He will harness a mixed variable multi-objective Bayesian optimisation algorithm to tackle the problem of simultaneously exploring continuous and discrete variable in the same optimisation, which if successful with represent a step change in this AI field.

Video
ai4sd_march_2022_day_3_ThomasChamberlain - Version of Record
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Video
ai4sd_march_2022_day_3_ThomasChamberlain
Available under License Creative Commons Attribution.
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More information

Published date: 3 March 2022
Additional Information: Dr Thomas Chamberlain comes from Matlock, in Derbyshire, and completed an MSci in Chemistry at the University of Nottingham in 2005. He was then awarded a University Interdisciplinary award to study a PhD with Professors Andrei Khlobystov and Neil Champness in Chemistry and Peter Beton in the School of Physics working on the synthesis of novel functional fullerene molecules and the subsequent formation of fullerene/carbon nanotube peapod structures. He received his PhD in 2009 and then joined the Nottingham Nanocarbon group as a post-doctoral research associate studying the use of supramolecular forces, such as van der Waals and H-bonding, to organise molecules in 1D and 2D arrays utilising carbon nanotubes as quasi 1D templates. During this position he established the application of carbon nanotubes as catalytic nanoreactors for the formation of novel molecular and nanostructured products and developed a wide variety of techniques to study the interactions of carbon and metal species at both atomic and bulk length scales. Dr Chamberlain moved to the University of Leeds in 2015, where he is currently an Associate Professor with an established independent research group within the Institute of Process Research and Development applying his understanding of nanomaterials to both fundamental and applied research challenges.
Venue - Dates: AI4SD Network+ Conference, Chilworth Manor , Southampton, United Kingdom, 2022-03-01 - 2022-03-03

Identifiers

Local EPrints ID: 469297
URI: http://eprints.soton.ac.uk/id/eprint/469297
PURE UUID: a1263364-5cf2-4ab3-8334-4ee2ddae9559
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302
ORCID for Samantha Kanza: ORCID iD orcid.org/0000-0002-4831-9489
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 13 Sep 2022 16:38
Last modified: 14 Sep 2022 01:56

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Contributors

Author: Thomas Chamberlain
Editor: Jeremy G. Frey ORCID iD
Editor: Samantha Kanza ORCID iD
Editor: Mahesan Niranjan ORCID iD

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