Abstracts Book - IMRC 2018

• SD1-O020 Invited Talk
DATA-DRIVEN MOLECULAR ENGINEERING OF SOLAR-POWERED
WINDOWS
Jacqueline Cole 1,2
1 ISIS Facility, STFC Rutherford Appleton Laboratory, United Kingdom, Channel Islands & Isle of
Man. 2 University of Cambridge, Physics, United Kingdom, Channel Islands & Isle of Man.
Large-scale data-mining workflows are increasingly able to predict successfully
new chemicals that possess a targeted functionality. The success of such
materials discovery approaches is nonetheless contingent upon having the right
data source to mine, adequate supercomputing facilities and workflows to
enable this mining, and algorithms that suitably encode structure-function
relationships as data-mining workflows which progressively short list data
toward the prediction of a lead material for experimental validation.
This talk describes how to met these data science requirements via a large-scale
data-mining case study that aims to discover new materials for solar-powered
windows. In particular, the presentation shows how to auto-generate large
material databases of photovoltaic-relevant experimental information
documents, using natural language processing and machine learning, via our
ChemDataExtractor tool [1,2]. Machine learning is then employed to populate
any missing experimental data.
A workflow that executes large-scale electronic structure calculations to afford
a computational counterpart to these experimental data is then described.
These wavefunction calculations are used to extend knowledge beyond
experiment. The resulting large database of chemical structures and their
optical properties is then mined for materials discovery using algorithms that
are encoded forms of structure-function relationships. These molecular design
rules progressively filter the parent set of chemicals until a lead candidate
appears, which is experimentally validated.
References:
[1] M. C. Swain, J. M. Cole, ChemDataExtractor: A Toolkit for Automated
Extraction of Chemical Information the Scientific Literature, J. Chem. Inf.
Model., 2016, 56 (10), pp 1894–1904.
[2] www.chemdataextractor.org