Designing display materials without a wet lab: progress in machine learning

Machine learning (ML) and Artificial Intelligence (AI) have made significant advances in chemical design. Autoencoders and Convoluted Neural Networks have allowed the abstraction of chemical structures into defined latent space that can be optimized or correlated to complex chemical properties. This has led to the assumption that ML will enable rapid design of new chemical compounds. Unfortunately, ML models often require hundreds of thousands of well-defined data points for training, which is often difficult to get experimentally, or impossible for novel materials. Due to the lack of experimental data, ML models are often trained on data from quantum chemistry simulations such as Density Functional Theory. ML models trained on quantum chemistry simulations inherit the issues embedded into these quantum chemistry theories without careful consideration of the bias that exists within quantum chemistry methods, and thus only accelerates our convergence to the wrong answer. This requires scientists to develop accurate quantum chemistry methods to generate accurate synthetic data to support ML algorithms. Here we present a novel quantum computing inspired method – the iterative Qubit Coupled Cluster method - that shows strong correlation between functional group changes and observed phosphorescent emission in Organic Light Emitting Diodes. We will discuss how these methods can be used to generate accurate and consistent databases for ML training.