Materials and Megabytes

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

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Exploring the development of machine learning for materials science, physics, and chemistry applications through conversation with researchers at the forefront of this growing interdisciplinary field. Brought to you in collaboration by the Stanford Materials Computation and Theory Group and Qian Yang's lab at the University of Connecticut.

13/01/2020

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

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...

23 phút
14/09/2019

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. Du...

24 phút
09/04/2019

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 Continuum. ...

19 phút
15/02/2019

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, E....

24 phút
25/01/2019

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)Rupp, M....

23 phút
24/01/2019

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., Gaussianapproximation 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., Permutatio...

40 phút
03/07/2018

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

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

20 phút
02/07/2018

Ekin Dogus Cubuk (Season 1, Ep. 2)

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

28 phút
01/07/2018

Kieron Burke (Season 1, Ep. 1)

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

14 phút
30/06/2018

Introduction (Season 1, Ep. 0)

Start here for a brief introduction to this podcast!

2 phút

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Stanford Materials Computation and Theory Group, Qian Yang's lab at the University of Connecticut
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2019 - 2020
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10
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Materials and Megabytes

Materials and Megabytes

Tập

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

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

Turab Lookman (Season 2, Ep.4)

Patrick Riley (Season 2, Ep. 3)

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

Gábor Csányi (Season 2, Ep. 1)

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

Ekin Dogus Cubuk (Season 1, Ep. 2)

Kieron Burke (Season 1, Ep. 1)

Introduction (Season 1, Ep. 0)

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