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Active Learning via Bayesian Optimization for Materials Discovery

2021.06.16 Hieu Doan, Garvit Agarwal, Argonne National Laboratory Part of Hands-on Data Science and Machine Learning Training Series at: https://nanohub.org/groups/ml/handson... Discovery of new and improved materials is an essential aspect of materials science. However, a common challenge faced by various disciplines is the large search space that often renders both high-throughput experiments and simulations intractable. One potential solution is to employ active learning, a semi-supervised machine learning approach, to efficiently explore the search space with minimal number of candidate evaluations. In this tutorial, we will demonstrate the use of active learning via Bayesian optimization (BO) to identify ideal molecular candidates for an energy storage application. Our step-by-step walkthrough of the code will go over the following: 1 Preprocessing the candidate database: • Feature generation • Dimensionality reduction 2 Running the BO cycles: • Uncertainty prediction via Gaussian Process Regression • Acquisition function evaluation • Selection of new candidate 3 Tuning the model performance The tool Bayesian optimization tutorial using Jupyter notebook can be found on nanoHUB.org at: https://nanohub.org/resources/bayesopt This talk and additional downloads can be found on nanoHUB.org at: https://nanohub.org/resources/35144