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

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 $H_{2}S{O}_4$ electrolyte.

References in corpus (16)

• Automatic chemical design using a data-driven continuous representation of molecules
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• The Alloy Theoretic Automated Toolkit: A User Guide
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• Multicomponent multisublattice alloys, nonconfigurational entropy and other additions to the Alloy Theoretic Automated Toolkit
• Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
• Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
• Predicting materials properties without crystal structure: Deep representation learning from stoichiometry
• Cu-Au, Ag-Au, Cu-Ag and Ni-Au intermetallics: First-principles study of phase diagrams and structures
• Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materials
• Developing an improved Crystal Graph Convolutional Neural Network framework for accelerated materials discovery
• icet - A Python library for constructing and sampling alloy cluster expansions
• An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
• Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
• Construction of Energy Functions for Lattice Heteropolymer Models: A Case Study in Constraint Satisfaction Programming and Adiabatic Quantum Optimization

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