Accelerated chemical space search using a quantum-inspired cluster expansion approach

TL;DR

This paper introduces a novel quantum-inspired cluster expansion method that accelerates chemical space searches for materials discovery, achieving 10-50 times faster results and identifying promising new catalysts with superior activity and stability.

Contribution

The study combines cluster expansion with quantum-inspired superposition to enable quantum annealers in chemical space exploration, significantly improving search speed and accuracy.

Findings

Achieved 10-50 times faster chemical space search compared to traditional algorithms.

Discovered a new Ru-Cr-Mn-Sb-O2 catalyst family with high activity.

Found a catalyst with 8 times higher activity than the current standard.

Abstract

To enable the accelerated discovery of materials with desirable properties, it is critical to develop accurate and efficient search algorithms. Quantum annealers and similar quantum-inspired optimizers have the potential to provide accelerated computation for certain combinatorial optimization challenges. However, they have not been exploited for materials discovery due to absence of compatible optimization mapping methods. Here we show that by combining cluster expansion with a quantum-inspired superposition technique, we can lever quantum annealers in chemical space exploration for the first time. This approach enables us to accelerate the search of materials with desirable properties order 10-50 times faster than genetic algorithms and bayesian optimizations, with a significant improvement in ground state prediction accuracy. Levering this, we search chemical space for discovery of acidic oxygen evolution reaction (OER) catalysts and find a promising previously unexplored chemical family of Ru-Cr-Mn-Sb-O$_2$. The best catalyst in this chemical family show a mass activity 8 times higher than state-of-art RuO$_2$ and maintain performance for 180 hours while operating at 10mA/cm$^2$ in acidic 0.5 M $H_2SO_4$ electrolyte.