  While machine learning (ML) has shown increasing effectiveness in optimizing
materials properties under known physics, its application in challenging
conventional wisdom and discovering new physics still remains challenging due
to its interpolative nature. In this work, we demonstrate the potential of
using ML for such applications by implementing an adaptive ML-accelerated
search process that can discover unexpected lattice thermal conductivity
($\kappa_l$) enhancement instead of reduction in aperiodic superlattices (SLs)
as compared to periodic superlattices. We use non-equilibrium molecular
dynamics (NEMD) simulations for high-fidelity calculations of $\kappa_l$ for a
small fraction of SLs in the search space, along with a convolutional neural
network (CNN) which can rapidly predict $\kappa_l$ for a large number of
structures. To ensure accurate prediction by the CNN for the target unknown
structures, we iteratively identify aperiodic SLs containing structural
features which lead to locally enhanced thermal transport, and include them as
additional training data for the CNN in each iteration. As a result, our CNN
can accurately predict the high $\kappa_l$ of aperiodic SLs that are absent
from the initial training dataset, which allows us to identify the previously
unseen exceptional structures. The identified RML structures exhibit increased
coherent phonon contribution to thermal conductivity owing to the presence of
closely spaced interfaces. Our work describes a general purpose machine
learning approach for identifying low-probability-of-occurrence exceptional
solutions within an extremely large subspace and discovering the underlying
physics.
