Roberto Covino, Frankfurt Institute for Advanced Studies, Frankfurt, Germany
I will present an artificial intelligence (AI) agent that learns molecular mechanisms from computer simulations. The agent integrates path theory, transition path sampling (TPS), deep learning, and symbolic AI to sample complex molecular self-organization phenomena and learn how to predict their outcome. TPS is a Markov Chain Monte Carlo method to sample the rare trajectories connecting metastable states. Using reinforcement learning, the agent iteratively trains a deep neural network on the outcomes of TPS simulation attempts.