Materials and Megabytes

Stanford Materials Computation and Theory Group, Qian Yang's lab at the University of Connecticut

Exploring the development of machine learning for materials science, physics, and chemistry applications through conversation with researchers at the forefront of this growing interdisciplinary field. Brought to you in collaboration by the Stanford Materials Computation and Theory Group and Qian Yang's lab at the University of Connecticut.

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Materials and Megabytes

Episodes (10)

Jan 13, 2020 Paper Interview - Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
Sep 14, 2019 Paper interview - Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data
Apr 9, 2019 Turab Lookman (Season 2, Ep.4)

Materials and Megabytes

Jan 13, 2020
24¾ minutes
16 MB

We discuss the paper Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models with the author Prof. Heather J. Kulik. Papers discussed in this episode: (Main discussion) Duan, C.; Janet, J. P.; Liu, F.; Nandy, A.; Kulik, H. J. Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models. J. Chem. Theory Comput. 2019, 15 (4), 2331–2345. https://doi.org/10.1021/acs.jctc.9b00057.(More on uncertainty metrics in latent space) Janet, J. P.; Duan, C.; Yang, T.; Nandy, A.; Kulik, H. J. A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery. Ch...

Materials and Megabytes

Paper Interview - Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

Stanford Materials Computation and Theory Group, Qian Yang's lab at the University of Connecticut

00:00 -22:58