Active Learning for Materials Design with Kevin Tran - TWiML Talk #238

In this insightful talk, Kevin Tran, a PhD student in Chemical Engineering at Carnegie Mellon University, shares his expertise on the intersection of machine learning and renewable energy fuel cells. He discusses the challenges in creating effective electrocatalysts for CO2 reduction and H2 evolution. Kevin dives into the role of active learning in materials design, highlighting its iterative nature and reliance on quantum mechanics-based simulations. He also examines how advanced methodologies, like Bayesian statistics and neural networks, improve predictive accuracy in catalyst performance.