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