SOURCE-LINKED INTELLIGENCE

A Synthesis Oracle for Scalable and Sustainable Porous Nanomaterials

CORDIS · observation · Publication date unknown

esentation for the synthesis of the common MOF UiO-66, creating a database of synthesis methods connected to to material quality profiles. Once created, I will develop a UiO-66 synthesis “oracle” – a machine learning model which can generate novel synthesis procedures from target material quality profiles. I will then refine the synthesis oracle functions experimentally using Bayesian active learning to identify the global optimum synthesis parameters with respect to several quality metrics. Finally, I will transfer the UiO-66 synthesis oracle to a family of related MOF materials, using further lab synthesis and active learning to fine-tune the quality of these secondary MOFs. This new paradigm in nanomaterial synthesis optimisation will be transformative in the field of synthesis optimisation and intensification, representing a novel approach that is generalisable to any synthesis target. In turn, completion of this fellowship will perfectly position me to transition into an independent academic career at the interface of digital chemistry and process intensification.

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recordType

award

status

SIGNED

region

EU

value

200400

unit

EUR