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,

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.;

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

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.co

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.’ arXiv:1707.04146, (2017)Ramak

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., Gaussianapproximation potentials: the accuracy of quantum mechanics, without the electrons.

Our guest on this episode is Professor Kieron Burke from the University of California, Irvine.

Our guest on this episode is Dr. Ekin Doğuş Çubuk from Google Brain.

Our guest on this episode is Professor Evan J. Reed from Stanford University.

Start here for a brief introduction to this podcast!