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Location
ZOOM
Speaker
Roberto Covino, Frankfurt Institute for Advanced Studies, Frankfurt, Germany
Host
Kananenka
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.
Location
ZOOM
Speaker
Jason Goodpaster, University of Minnesota
Host
Kananenka
Large, condensed phase, and extended systems impose a challenge for theoretical studies due to the compromise between accuracy and computational cost in their calculations. We present two methods that show exciting promise for treating this compromise, machine learning and quantum embedding, and apply these methods and others to the study of magnetic metal organic frameworks and electrocatalytic systems.
Location
ZOOM
Speaker
Carmen Domene, University of Bath, UK
Host
Kananenka
Ion channels are ubiquitous membrane-embedded transport proteins crucial for life. The central function of ion channels lies on their ability to selectively transport ions given the appropriate stimuli e.g. voltage, mechanical force, temperature or pH, and provided their regulatory gates are open. Gating mechanisms can be further modulated by ligands, a fact consistent with the fine tuning of their activity to molecular cues and organism demands.