{"id":102560,"date":"2024-11-14T08:39:23","date_gmt":"2024-11-14T14:39:23","guid":{"rendered":"https:\/\/engineering.wisc.edu\/?post_type=tribe_events&p=102560"},"modified":"2024-11-14T08:39:26","modified_gmt":"2024-11-14T14:39:26","slug":"cbe-seminar-series-joel-paulson","status":"publish","type":"tribe_events","link":"https:\/\/engineering.wisc.edu\/event\/cbe-seminar-series-joel-paulson\/","title":{"rendered":"CBE Seminar Series: Joel Paulson"},"content":{"rendered":"
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\n\t\t\n\t\t\tDecember 2, 2024\t\t<\/span>\n\n\t\t\t\t\t\n\t\t\t\t @ \t\t\t<\/span>\n\t\t\t\n\t\t\t\t4:00 PM\t\t\t<\/span>\n\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t – \t\t\t\t<\/span>\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t5:00 PM\t\t\t\t<\/span>\n\t\t\t\n\t\t\t\t\t\t<\/h2>\n<\/div>\n\n\n\n

Joel Paulson
Department of Chemical and Biomolecular Engineering
The Ohio State University
Columbus, OH<\/p>\n\n\n\n

Physics-Informed Bayesian Optimization: A Sequential Learning Framework for Accelerating Scientific Design and Discovery<\/h3>\n\n\n\n
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Bayesian optimization (BO) is a powerful tool for optimizing non-convex black-box functions that are expensive and\/or time-consuming to evaluate and subject to random noise in their observations. Many important real-world science and engineering problems belong to this class such as optimizing over high-fidelity simulations, tuning hyperparameters in machine learning algorithms, and material and drug discovery. Traditionally, BO has been deployed as a purely black-box optimizer. However, this black-box approach can lead to significant performance losses, especially in high-dimensional, intricately constrained design spaces, such as those appearing in materials and molecular optimization problems. In most real-world applications, however, only a portion of the model is unknown, creating opportunities for (significant) performance gains by effectively \u201cpeeking inside of the black box.\u201d In this talk, I will discuss \u201cphysics-informed\u201d Bayesian optimization (PIBO) methods recently developed by the Paulson Lab, which leverage known problem structure to achieve state-of-the-art performance. One such approach, MolDAIS, is designed for molecular property optimization under strict evaluation budgets. MolDAIS is built on the insight that relevant properties often depend on a small subset of molecular descriptors, which we show can be actively learned from data using sparsity-inducing probabilistic machine learning models. I will demonstrate MolDAIS\u2019s effectiveness on several molecular design benchmarks. I will also highlight an exciting new real-world experimental collaboration where we used a MolDAIS variant to find candidate materials with specific energy and cycling stability substantially exceeding current state-of-the-art organic electrodes in aqueous zinc-ion batteries, including candidates that are synthesizable at a fraction of the cost.<\/p>\n<\/div>\n\n\n

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