Overcoming the Limits of Approximate Electronic Structure Models in Machine Learning Accelerated Materials Discovery

Machine learning (ML)-accelerated discovery of transition metal containing materials such as light-harvesting chromophores, phosphors, and other photoactive complexes holds great promise. Nevertheless, the open shell d electrons that impart many of the desirable properties to these systems also make them notoriously challenging to study with conventional electronic structure techniques such as density functional theory (DFT). Thus, when ML is used to accelerate computational screening, it often inherits the biases of the underlying method used to generate training data. I will describe our recent efforts to overcome these limits through three complementary approaches. First, I will describe how we have developed machine learning models trained directly on experimental reports of iridium phosphors, leading to the development of ML models that can predict experimental emission energies and lifetimes with superior or equivalent performance to conventional methods such as time-dependent DFT but in a fraction of the computational time. Next, I will describe how we overcome limits of DFT uncertainty in screening for light harvesting chromophores with earth abundant 3d metals by incorporating method insensitivity into a multi-objective optimization workflow, requiring a consensus of functionals to agree on a property in order for a material to be selected as optimal. These workflows accelerate materials discovery by at least 1000-fold. Finally, I will describe our development of a density functional "recommender" that can identify which DFT functional is most predictive to obtain accurate vertical spin excitation energies in transition metal complexes.