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 in latent space) Janet, J. P.; Duan, C.; Yang, T.; Nandy, A.; Kulik, H. J. A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery. Chem. Sci. 2019, 10 (34), 7913–7922. https://doi.org/10.1039/C9SC02298H.
• (Follow-up paper with active learning) Janet, J. P.; Ramesh, S.; Duan, C.; Kulik, H. Accurate Multi-Objective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization. 2019. https://doi.org/10.26434/chemrxiv.11367572.v1.

Kulik group website: http://hjkgrp.mit.edu/

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. Duerloo, K.-A.; Cui, Y.; J. Reed, E. Holistic Computational Structure Screening of More than 12000 Candidates for Solid Lithium-Ion Conductor Materials. Energy & Environmental Science 2017, 10 (1), 306–320. https://doi.org/10.1039/C6EE02697D.
• Hinton, G.; Vinyals, O.; Dean, J. Distilling the Knowledge in a Neural Network. arXiv:1503.02531 [cs, stat] 2015.
• Zhou, Q.; Tang, P.; Liu, S.; Pan, J.; Yan, Q.; Zhang, S.-C. Learning Atoms for Materials Discovery. PNAS 2018, 115 (28), E6411–E6417. https://doi.org/10.1073/pnas.1801181115.
• Sendek, A. D.; Cheon, G.; Pasta, M.; Reed, E. J. Quantifying the Search for Solid Li-Ion Electrolyte Materials by Anion: A Data-Driven Perspective. arXiv:1904.08996 [cond-mat, physics:physics] 2019.

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. Acta Materialia 2019, 168, 473–510. https://doi.org/10.1016/j.actamat.2019.01.051.
• Lookman, T.; Balachandran, P. V.; Xue, D.; Yuan, R. Active Learning in Materials Science with Emphasis on Adaptive Sampling Using Uncertainties for Targeted Design. npj Computational Materials 2019, 5 (1), 21. https://doi.org/10.1038/s41524-019-0153-8.
• Xue, D.; Balachandran, P. V.; Hogden, J.; Theiler, J.; Xue, D.; Lookman, T., Accelerated Search for Materials with Targeted Properties by Adaptive Design. Nature Communications 2016, 7, 11241. https://doi.org/10.1038/ncomms11241.

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., The Unreasonable Effectiveness of Mathematics in the Natural Sciences. Communications in Pure and Applied Mathematics, doi:10.1002/cpa.3160130102 (1960)
• Gulshan, V., Peng, L, Coram, M., Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. The Journal of the American Medical Association, doi:10.1001/jama.2016.17216 (2016)

Google Accelerated Science website: ai.google/research/teams/applied-science/gas

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., Tkatchenko, A., Müller, K.-R., and von Lilienfeld, O. A., Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Physical Review Letters, doi:10.1103/PhysRevLett.108.058301 (2012)

Group website: https://www.chemie.unibas.ch/~anatole/

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., Permutationally invariant potential energy surfaces in high dimensionality. International Reviews in Physical Chemistry, doi:10.1080/01442350903234923 (2009)

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

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

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

Start here for a brief introduction to this podcast!