Machine Learning and Reinforcement Learning for Automated Experimentation and Materials Synthesis

Recent advances in the areas of machine learning and reinforcement learning have led to
tremendous increases in accuracy and efficacy of ‘AI’ in fields such as computer vision, natural
language processing and gameplay. Here, we will focus on the applications and developments of
these methods as pertaining to physical sciences with several textbook cases. First, we show that
utilizing non-parametric models in conjunction with Bayesian optimization can substantially
increase the efficiency of data collection with scanning probe microscopy instruments. This is
illustrated for an example of finding areas of high electromechanical response in a thin
ferroelectric film. At the same time, improvements in machine learning and edge devices can
also be leveraged to accelerate the acquisition by performing parameter estimation in near real-
time, obviating the need for more expensive post-processing and enabling faster feedback for the
user. Finally, recent advances in the field of reinforcement learning can be leveraged to
dramatically alter the prevailing materials synthesis paradigm. A multi-agent reinforcement
learning algorithm was modified to work at scale and was utilized to tackle problems of
materials synthesis from the bottom-up in simulated environments of dopants in a graphene
lattice, as well as adjusting deposition parameters for a simulated kinetic Monte-Carlo
environment for thin-film deposition.