Data-driven Molecular Engineering of Functional Materials - Joint Chemistry/Physics colloquium

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Jacqui Cole
Data -driven Molecular Engineering of Functional Materials
Jacqueline M. Cole, a ,b,c a Cavendish Laboratory, Department of Physics, University of Cambridge, UK. b Depart ment of Chemical Engineering & Biotechnology, University of Cambridge , UK c ISIS Neutron and Muon Facility, STFC Rutherford Appleton Laboratory, UK.
The world needs new materials to stimulate industry in key sectors of our economy: environment and sustainability, information storage, optoelectronics, efficiency of chemical proce sses. Yet, nearly all functional materials are still discovered by ‘trial -and -error’, whose lack of predictability affords a major materials bottleneck to technological innovation. The emerging field of data - driven molecular engineering offers a prospectiv e solution to this problem; thereby, systematic molecular design and engineering strategies are encoded into algorithms that search through massive {chemical -property} datasets to discover a material that suits a bespoke application. Such data -science approaches to materials discovery are only just becoming possible, given recent advances in artificial intelligence, rapid rises in high -performance -computi ng capacities , and changes in government legislation that regulates the open -acces s of scientific data. This talk will present a range of data -driven materials -by -design capabilities that are being developed by the Molecular Engineering group at Cambridge, to accelerate the discovery of new materials. The materials discovery pipeline fe atures the coupling of niche database auto -generation tools, such as ChemDataExtractor [1,2], with machine learning capabilities and the custom design of algorithms to predict new functional material s; we then experimentally validate the predictions using a range of advanced materials characterisation [3] and device testing methods. The discovery of new light -harvesting materials for dye -sensitized sola r cell s will act a s a case study to illustrate the se design -to -device methods. Reference s [1] M. C. Swain, J. M. Cole, ChemDataExtractor: A Toolkit for Automated Extraction of Chemical Information from the Scientific Literature, J. Chem. Inf. Model. , 2016 , 56 (10), pp 1894 –1904. [2] C. J. Court, J. M. Cole, Auto -generated materials database of Curie and Néel temperatures via semi -supervised relationship extraction, Scientific Data , 2017 , 5:180111 | DOI: 10.1038/sdata.2018.111 [3] J. McCree -Grey, J. M. Cole, S. A. Holt, P. J. Evans, Y. Gong , Dye ...TiO 2 Interfacial Structure of Dye -Sensitised Solar Cell Working Electrodes Buried under a Solution of I-/I3- Redox Electrolyte, Nanoscale , 2017 , 9, 11793 -11805.
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  • Venue

    Lecture Theatre C, Physics

  • Date

    October 5, 2018

  • Time

    From: 10h00 To: 11h00

  • Sponsor

    University of St Andrews
    The oldest university in Scotland, with international renown for both research and education of undergraduates and postgraduates.

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