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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. Chem. Sci. 2019, 10 (34), 7913–7922. https://doi.org/10.1039/C9SC02298H.
- (Follow-up paper with active learning) Janet, J. P.; Ramesh, S.; Duan, C.; Kulik, H. Accurate Multi-Objective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization. 2019. https://doi.org/10.26434/chemrxiv.11367572.v1.
Kulik group website: http://hjkgrp.mit.edu/
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ตอนอื่นๆ ใน PODCAST นี้
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Bartok, A. P., Payne, M. C., Kondor, R., and Csanyi, G., Gaussianapproximation potentials: the accuracy of quantum mechanics, without the electrons. Physical Review Letters, doi:10.1103/PhysR…
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Huang, B., and von Lilienfeld, O. A., The ‘DNA’ of Chemistry: Scalable Quantum Machine Learning with ‘Amons.’ arXiv:1707.04146, (2017)
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ข้อสงวนสิทธิ์: พอดแคสต์และอาร์ตเวิร์คที่ฝังอยู่ในหน้านี้มาจาก Stanford Materials Computation and Theory Group, Qian Yang's lab at the University of Connecticut ซึ่งเป็นทรัพย์สินของเจ้าของและไม่มีส่วนเกี่ยวข้องหรือรับรองโดย Listen Notes, Inc.
