Better and faster design of organic light emitting materials using machine learning and quantum computing

Machine Learning

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(A) Structure of Alq3, (B) Flowchart of two quantum optimization algorithms chosen by the authors, (C) Illustration of the authors’ method for reducing the effects of device noise.Credit: QI Gao et al.

Over the past decade, organic light-emitting materials have been recognized by both academia and industry as promising components for lightweight, flexible and versatile optoelectronic devices such as OLED displays. However, finding suitably efficient materials is difficult.

To address this challenge, the collaborative research team developed a novel approach that combines machine learning models and quantum-classical molecular design to accelerate the discovery of efficient OLED emitters. This study was published on his May 17th. intelligent computing.

The optimal OLED emitters discovered by the authors using this ‘hybrid quantum-classical procedure’ are the deuterated derivatives of Alq.3 It has very high luminous efficiency and can be synthesized.

A deuterated OLED emitter is an organic material in which the hydrogen atoms in the emitter molecule have been replaced with deuterium atoms. Although they have the potential to emit light very efficiently, designing such deuterated OLED emitters presents a computational challenge. This challenge arises from the need to optimize the positions of the deuterium atoms within the emitter molecule, and the calculations have to be performed from scratch.

New workflows involving both classical and quantum computers speed up these computations. First, we perform quantum chemical calculations on a classical computer and obtain the “quantum efficiency” of a set of deuterated Alqs.3 molecule. These data on the luminous efficiencies of various molecules are used to create training and test datasets for building machine learning models for predicting the quantum efficiencies of various deuterated Alqs.3 molecule.

A machine learning model is then used to construct the system’s energy function, known as the Hamiltonian. Quantum optimization is then performed on a quantum computer using two quantum variational optimization algorithms, variational quantum eigensolver (VQE) and quantum approximate optimization algorithm (QAQA), to achieve optimal quantum efficiency. Assist machine learning to discover molecules with Synthesis constraints are introduced during the quantum optimization process to ensure that the optimized molecule is synthesizable.

To improve the accuracy of predictions on quantum devices, the authors employ a noise-tolerant technique called recursive random variable elimination (RPVE), which states that “quantum devices can be used to find optimal weights with very high accuracy. We succeeded in finding hydrogenated molecules. Furthermore, they point out that combining this new noise-immune technique with two of his selected quantum optimization algorithms could potentially achieve quantum advantages in computing for quantum devices in the near future.

In general, the authors hope that a combined approach of quantum chemistry, machine learning and quantum optimization could create “new opportunities to generate and optimize key molecules for materials informatics.” .

For more information:
Qi Gao et al., Quantum Classical Computational Molecular Design of Deuterated Efficient OLED Emitters, intelligent computing (2023). DOI: 10.34133/icomputing.0037

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