  Improving the efficiency and accuracy of energy calculations has been of
significant and continued interest in the area of materials informatics, a
field that applies machine learning techniques to computational materials data.
Here, we present a heuristic quantum-classical algorithm to efficiently model
and predict the energies of substitutionally disordered binary crystalline
materials. Specifically, a quantum circuit that scales linearly in the number
of lattice sites is designed and trained to predict the energies of quantum
chemical simulations in an exponentially-scaling feature space. This circuit is
trained by classical supervised-learning using data obtained from
classically-computed quantum chemical simulations. As a part of the training
process, we introduce a sub-routine that is able to detect and rectify
anomalies in the input data. The algorithm is demonstrated on the complex
layer-structured of Li-cobaltate system, a widely-used Li-ion battery cathode
material component. Our results shows that the proposed quantum circuit model
presents a suitable choice for modelling the energies obtained from such
quantum mechanical systems. Furthermore, analysis of the anomalous data
provides important insights into the thermodynamic properties of the systems
studied.
