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 metri...
Jan 13, 2020•23 min•Season 3Ep. 2
We discuss the paper Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data with the authors Dr. Ekin Dogus Cubuk and Dr. Austin D. Sendek. Papers discussed in the episode: Cubuk, E. D.; Sendek, A. D.; Reed, E. J. Screening Billions of Candidates for Solid Lithium-Ion Conductors: A Transfer Learning Approach for Small Data. J. Chem. Phys. 2019 , 150 (21), 214701. https://doi.org/10.1063/1.5093220 . Sendek, A. D.; Yang, Q.; D. Cubuk, E.; N. ...
Sep 14, 2019•24 min•Season 3Ep. 1
Our guest on this episode is Dr. Turab Lookman from Los Alamos National Laboratory. The interview took place at the 2018 MRS Fall meeting. Relevant papers: Gubernatis, J. E.; Lookman, T., Machine Learning in Materials Design and Discovery: Examples from the Present and Suggestions for the Future. Phys. Rev. Materials 2018 , 2 (12), 120301. https://doi.org/10.1103/PhysRevMaterials.2.120301 . Rickman, J. M.; Lookman, T.; Kalinin, S. V., Materials Informatics: From the Atomic-Level to the Continuum...
Apr 09, 2019•19 min•Season 2Ep. 4
Our guest on this episode is Dr. Patrick Riley from Google Accelerated Science. Some relevant papers and links: Riley, P., Practical advice for analysis of large, complex data sets. The Unofficial Google Data Science Blog , www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html (2016) Zinkevich, M., Rules of Machine Learning: Best Practices for ML Engineering. https://developers.google.com/machine-learning/guides/rules-of-ml/ (last updated Oct 2018) Wigner, E., T...
Feb 15, 2019•24 min•Season 2Ep. 3
Our guest for this episode is Prof. Dr. O. Anatole von Lilienfeld from the University of Basel. Some relevant papers: Huang, B., and von Lilienfeld, O. A., The ‘DNA’ of Chemistry: Scalable Quantum Machine Learning with ‘Amons.’ a rXiv:1707.04146, (2017) Ramakrishnan, R., Dral, P. O., Rupp, M., and von Lilienfeld, O. A., Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach. Journal of Chemical Theory and Computation, doi:10.1021/acs.jctc.5b00099 (2015) Rupp, M., Tkatch...
Jan 25, 2019•23 min•Season 2Ep. 2
Our guest on this episode is Professor Gábor Csányi from the University of Cambridge. Some relevant papers: Bartok, A. P., Payne, M. C., Kondor, R., and Csanyi, G., Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Physical Review Letters , doi:10.1103/PhysRevLett.104.136403 (2010) Bartok, A. P., Kondor, R., and Csanyi, G., On representing chemical environments. Phys. Rev. B , doi:10.1103/PhysRevB.87.184115 (2013) Braams, B. J., and Bowman, J. M., Permu...
Jan 24, 2019•40 min•Season 2Ep. 1
Our guest on this episode is Professor Evan J. Reed from Stanford University.
Jul 03, 2018•20 min•Season 1Ep. 4
Our guest on this episode is Dr. Ekin Doğuş Çubuk from Google Brain.
Jul 02, 2018•28 min•Season 1Ep. 3
Our guest on this episode is Professor Kieron Burke from the University of California, Irvine.
Jul 01, 2018•14 min•Season 1Ep. 2
Start here for a brief introduction to this podcast!
Jun 30, 2018•2 min•Season 1Ep. 1
