<description>&lt;p&gt;We discuss the paper Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models with the author Prof. Heather J. Kulik.&lt;br/&gt;&lt;br/&gt;Papers discussed in this episode:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;(Main discussion) Duan, C.; Janet, J. P.; Liu, F.; Nandy, A.; Kulik, H. J. &lt;em&gt;Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models.&lt;/em&gt; J. Chem. Theory Comput. &lt;b&gt;2019&lt;/b&gt;, 15 (4), 2331–2345. &lt;a href='https://doi.org/10.1021/acs.jctc.9b00057'&gt;https://doi.org/10.1021/acs.jctc.9b00057&lt;/a&gt;.&lt;/li&gt;&lt;li&gt;(More on uncertainty metrics in latent space) Janet, J. P.; Duan, C.; Yang, T.; Nandy, A.; Kulik, H. J. &lt;em&gt;A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery.&lt;/em&gt; Chem. Sci. &lt;b&gt;2019&lt;/b&gt;, 10 (34), 7913–7922. &lt;a href='https://doi.org/10.1039/C9SC02298H'&gt;https://doi.org/10.1039/C9SC02298H&lt;/a&gt;.&lt;/li&gt;&lt;li&gt;(Follow-up paper with active learning) Janet, J. P.; Ramesh, S.; Duan, C.; Kulik, H. &lt;em&gt;Accurate Multi-Objective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization.&lt;/em&gt; &lt;b&gt;2019&lt;/b&gt;. &lt;a href='https://doi.org/10.26434/chemrxiv.11367572.v1'&gt;https://doi.org/10.26434/chemrxiv.11367572.v1&lt;/a&gt;.&lt;/li&gt;&lt;/ul&gt;&lt;p&gt;Kulik group website: &lt;a href='http://hjkgrp.mit.edu/'&gt;http://hjkgrp.mit.edu/&lt;/a&gt;&lt;/p&gt;</description>

Materials and Megabytes

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

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

JAN 13, 202022 MIN
Materials and Megabytes

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

JAN 13, 202022 MIN

Description

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/