  Data driven materials discovery and optimization requires databases that are
error free and experimentally verified. Performing material measurements are
time-consuming and often restricted by the fact that material sample
preparations are non-trivial, labour-intensive and expensive. Numerical
modelling of materials has been studied over the years in order to address
these issues and nowadays it has been developed at multi-scale and
multi-physics levels. However, numerical models for nano-composites, especially
for ferroelectrics are limited due to multiple unknowns including oxygen
vacancy densities, grain sizes and domain boundaries existing in the system. In
this work, we introduce a human-machine interactive learning framework by
developing a scalable semi-empirical model to accurately predict material
properties enabled by deep learning (DL). MgO-doped BST (BaxSr1-xTiO3) is
selected as an example ferroelectric-dielectric composite for validation. The
DL model transfer-learns the experimental features of materials from a
measurement database which includes data for over 100 different ferroelectric
composites collected by screening the published data and combining our own
measurement data. The trained DL model is utilized in providing feedback to
human researchers, who then refine computer model parameters accordingly, hence
completing the interactive learning cycle. Finally, the developed DL model is
applied to predict and optimise new ferroelectric-dielectric composites with
the highest figure of merit (FOM) value.
