Quantum-Classical Computational Molecular Design of Deuterated High-Efficiency OLED Emitters

TL;DR

This paper introduces a hybrid quantum-classical computational method for designing deuterated OLED emitters with high efficiency, combining classical chemistry, machine learning, and quantum algorithms to optimize molecular properties.

Contribution

It presents a novel integrated approach using classical chemistry, machine learning, and quantum algorithms to predict and optimize OLED emitter efficiencies.

Findings

Accurately predicts emission efficiencies for 64 deuterated $Alq_3$ emitters with minimal training data.

Demonstrates quantum algorithms can identify optimal molecules with high probability on simulators.

Improves quantum optimization success rates by mitigating readout errors and applying binary search routines.

Abstract

This study describes a hybrid quantum-classical computational approach for designing synthesizable deuterated $Alq_3$ emitters possessing desirable emission quantum efficiencies (QEs). This design process has been performed on the tris(8-hydroxyquinolinato) ligands typically bound to aluminum in $Alq_3$. It involves a multi-pronged approach which first utilizes classical quantum chemistry to predict the emission QEs of the $Alq_3$ ligands. These initial results were then used as a machine learning dataset for a factorization machine-based model which was applied to construct an Ising Hamiltonian to predict emission quantum efficiencies on a classical computer. We show that such a factorization machine-based approach can yield accurate property predictions for all 64 deuterated $Alq_3$ emitters with 13 training values. Moreover, another Ising Hamiltonian could be constructed by including synthetic constraints which could be used to perform optimizations on a quantum simulator and device using the variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAOA) to discover a molecule possessing the optimal QE and synthetic cost. We observe that both VQE and QAOA calculations can predict the optimal molecule with greater than 0.95 probability on quantum simulators. These probabilities decrease to 0.83 and 0.075 for simulations with VQE and QAOA, respectively, on a quantum device, but these can be improved to 0.90 and 0.084 by mitigating readout error. Application of a binary search routine on quantum devices improves these results to a probability of 0.97 for simulations involving VQE and QAOA.