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,

Paper interview - Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data

Sep 14th, 2019 23m 55s

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.; Sen

Turab Lookman (Season 2, Ep.4)

Apr 9th, 2019 18m 59s

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: Exampl

Patrick Riley (Season 2, Ep. 3)

Feb 15th, 2019 24m 28s

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.unofficialgoogledat

O. Anatole von Lilienfeld (Season 2, Ep. 2)

Jan 25th, 2019 22m 49s

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,

Gábor Csányi (Season 2, Ep. 1)

Jan 24th, 2019 40m 26s

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