Generative Organic Electronic Molecular Design via Reinforcement Learning Integration with Quantum Chemistry: Tuning Singlet and Triplet Energy Levels

03 July 2023, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Generative molecular design strategies have emerged as promising alternatives to trial-and-error approaches for exploring and optimizing within large chemical spaces. To date, generative models with reinforcement learning approaches have frequently used low-cost methods to evaluate the quality of the generated molecules, enabling many loops through the generative model. However, for functional molecular materials tasks, such low-cost methods are either not available or would require the generation of large amounts of training data to train surrogate machine learning models. In this work, we develop a framework that connects the REINVENT reinforcement learning framework with excited state quantum chemistry calculations to discover molecules with specified molecular excited state energy levels, specifically molecules with excited state landscapes that would serve as promising singlet fission or triplet-triplet annihilation materials. We employ a two-step curriculum strategy to find a set of diverse promising molecules, then exploit a more focused chemical space with anthracene derivatives. Under this protocol, we show that the framework can find desired molecules and improve Pareto fronts for targeted properties versus synthesizability. Moreover, from this framework, we are able to find several different design principles used by chemists for the design of singlet fission and triplet-triplet annihilation molecules.


Inverse Materials Design
Reinforcement Learning
Singlet Fission

Supplementary materials

Supporting Information
Supporting information contains a description of the contents in all raw data files on the GitHub repository, Implementation Details for REINVENT, a Comparison of SCScore between molecules generated by REINVENT and the initial calibration set, the scoring functions used for evaluating excited state energy gaps, and Benchmark results for the excited state quantum chemistry calculations.

Supplementary weblinks


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