Our first publication on machine learning for materials science

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).