Abstracts Book - IMRC 2018

ABSTRACT
CAPTA-DRIVEN MATERIALS DESIGN
Even though it is always there, dealing with the complexity of nanomaterials,
the diversity of individual samples, and the persistent imperfection of individual
structures has been secondary to our search for novel properties and promising
applications. However, for our science to translate into technology we will inevitably
need to deal with the issue of polydispersivity and integrate this feature into the
next generation of more realistic structure/property predictions. Our predictions
need a fault tolerance, but uncovering the underlying connection between specific
structures and properties is difficult to do experimentally, particularly when many
(if not all) of the important design parameters are cross-correlated. Fortunately the
strategic use of computational methods and high performance computing can
provide these insights, and so much more. A range of reliable statistical and datadriven
methods, such as machine learning and deep learning, have become widely
available to help us to take greater advantage computational data, or “capta”.
Appropriate sampling of our enormous parameter spaces, judicious data cleaning
and curation, and selection of the right learning models are essential elements of
capta-driven materials design, and differ from approaches applied to experimental
data. In this presentation we will explore the development of capta sets and the
use of simple statistical and machine learning methods to predict properties and
performance. We will also see how to predict structure/property relationships for
entire samples of structures, and how we can investigate the impact of different
manufacturing processes that restrict the polydispersivity in different ways.
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