Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis

In this work, we applied Bayesian optimization methods to facilitate the search in a multi-dimensional parameter space of quantum dot synthesis and achieved the optimal results in a smaller number of experimental trials compared to conventional methods (random search, grid search, or gradient descent).