Materials and Megabytes

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

10 episodes

1 month ago

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 metr...

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Episodes (10/10)

Materials and Megabytes

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

5 years ago

22 minutes

Materials and Megabytes

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

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....

5 years ago

23 minutes

Materials and Megabytes

Turab Lookman (Season 2, Ep.4)

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 Continuu...

6 years ago

18 minutes

Materials and Megabytes

Patrick Riley (Season 2, Ep. 3)

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,...

6 years ago

24 minutes

Materials and Megabytes

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

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)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)Rup...

6 years ago

22 minutes

Materials and Megabytes

Gábor Csányi (Season 2, Ep. 1)

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...

6 years ago

40 minutes

Materials and Megabytes

Evan J. Reed (Season 1, Ep. 3)

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

7 years ago

19 minutes

Materials and Megabytes

Ekin Dogus Cubuk (Season 1, Ep. 2)

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