This project will investigate the application of multi-objective Bayesian optimization (specifically EHVI- and parEGO-based methods) to benchmark several multi-objective optimization tasks with QM9 dataset. The objective is to develop specific guidelines about the choice of surrogate and acquisition functions in the context of Multi-Objective Bayesian Optimization for molecular property optimization.

References:

1. Balandat, Maximilian and Karrer, Brian and Jiang, Daniel R. and Daulton, Samuel and Letham, Benjamin and Wilson, Andrew Gordon and Bakshy, Eytan (2020) BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
2. Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp & O. Anatole von Lilienfeld (2014) Quantum chemistry structures and properties of 134 kilo molecules