A Heuristic Quantum-Classical Algorithm for Modeling Substitutionally Disordered Binary Crystalline Materials

TL;DR

This paper introduces a heuristic quantum-classical algorithm that efficiently models energies of substitutionally disordered binary crystalline materials, leveraging a quantum circuit trained on classical data to improve accuracy and interpretability.

Contribution

The work presents a scalable quantum circuit model trained via classical supervised learning, with a novel anomaly detection routine, for modeling complex crystalline materials.

Findings

Quantum circuit scales linearly with lattice sites

Model accurately predicts energies of quantum chemical simulations

Anomaly detection offers insights into thermodynamic properties

Abstract

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.