Inc 2 - Machine Learning
Group Leaders
These observations suggest that ML methods can now be adapted to tackle the more generic challenge of materials discovery and design. In order to resolve the problem of extrapolation versus interpolation we plan to exploit that training and application of ML models is "agnostic" in the sense that origin and choice of the training data is irrelevant for formal construction of the ML model, due to its inherently inductive nature. As such, ML models can be improved or updated on a regular basis, through adaptation of training set size and composition, compound's representations, or locally adaptive regression functions. More specifically within this incubator project Inc 2 — Active Machine Learning for Computational Materials Design — we investigate possible ways to systematically accelerate the computational identification of promising materials candidates through combinations of supervised and unsupervised ML models, property optimization algorithms, and active learning.
Related publications (until January 2021)
- G. Domenichini, G. F. von Rudorff, O. A. von Lilienfeld, Effects of perturbation order and basis set on alchemical predictions, The Journal of Chemical Physics 153, 144118 (2020). [Open Access URL]
Dataset on Zenodo.
Group(s): von Lilienfeld / Project(s): INC2 - S. Parbhoo, M. Wieser, V. Roth, F. Doshi-Velez, Transfer Learning from Well-Curated to Less-Resourced Populations with HIV, 126, 589 (2020). [Open Access URL]
Group(s): Roth / Project(s): INC2 - G. F. von Rudorff, O. A. von Lilienfeld, Alchemical perturbation density functional theory, Physical Review Research 2, 023220 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - S. Heinen, M. Schwilk, G. F. von Rudorff, O. A. von Lilienfeld, Machine learning the computational cost of quantum chemistry, Machine Learning: Science and Technology 1, 025002 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - B. Huang, O. A. von Lilienfeld, Quantum machine learning using atom-in-molecule-based fragments selected on the fly, Nature Chemistry 12, 945+ (2020). [Open Access URL]
Dataset on Zenodo.
Group(s): von Lilienfeld / Project(s): INC2 - S. Käser, D. Koner, A. S. Christensen, O. A. von lilienfeld, M. Meuwly, Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL and PhysNet, The Journal of Physical Chemistry A 124, 8853 (2020). [Open Access URL]
Dataset on Zenodo.
Group(s): von Lilienfeld / Project(s): INC2 - M. Bragato, G. F. von Rudorff, O. A. von Lilienfeld, Data enhanced Hammett-equation: reaction barriers in chemical space, Chemical Science 11, 11859–11868 (2020). [Open Access URL]
Dataset on Zenodo.
Group(s): von Lilienfeld / Project(s): INC2 - M. Schwilk, D. N. Tahchieva, O. A. von lilienfeld, Large yet bounded: Spin gap ranges in carbenes, arXiv:2004.10600 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): von Lilienfeld / Project(s): INC2 - G. F. von Rudorff, S. N. Heinen, M. Bragato, O. A. von lilienfeld, Thousands of reactants and transition states for competing E2 and SN2 reactions, Machine Learning: Science and Technology 1, 045026 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): von Lilienfeld / Project(s): INC2 - A. S. Christensen, O. A. von lilienfeld, On the role of gradients for machine learning of molecular energies and forces, Machine Learning: Science and Technology 1, 045018 (2020). [Open Access URL]
Dataset on Materials Cloud.
Group(s): von Lilienfeld / Project(s): INC2 - B. Huang, O. A. von lilienfeld, Dictionary of 140k GDB and ZINC derived AMONs, arXiv:2008.05260 (2020). [Open Access URL]
Dataset on GitHub.
Group(s): von Lilienfeld / Project(s): INC2 - B. Parsaeifard, D. S. De, A. S. Christensen, F. A. Faber, E. Kocer, S. De, J. Behler, A. von Lilienfeld, S. Goedecker, An assessment of the structural resolution of various fingerprints commonly used in machine learning, Machine Learning: Science and Technology (2020). [Open Access URL]
Group(s): Goedecker, von Lilienfeld / Project(s): DD1, INC2 - M. Wieser, S. Parbhoo, A. Wieczorek, V. Roth, Inverse Learning of Symmetries, (2020). [Open Access URL]
Group(s): Roth / Project(s): INC2 - V. Nesterov, M. Wieser, V. Roth, 3DMolNet: A Generative Network for Molecular Structures, arXiv:2010.06477 (2020). [Open Access URL]
Group(s): Roth / Project(s): INC2 - S. Parbhoo, M. Wieser, A. Wieczorek, V. Roth, Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates, Entropy 22, 389 (2020). [Open Access URL]
Group(s): Roth / Project(s): INC2 - O. A. von Lilienfeld, K. Burke, Retrospective on a decade of machine learning for chemical discovery, Nature Communications 11, 4895 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - O. A. von Lilienfeld, K. Mueller, A. Tkatchenko, Exploring chemical compound space with quantum-based machine learning, Nature Reviews Chemistry 4, 347–358 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - A. Kilaj, H. Gao, D. Tahchieva, R. Ramakrishnan, D. Bachmann, D. Gillingham, O. A. von Lilienfeld, J. Kuepper, S. Willitsch, Quantum-chemistry-aided identification, synthesis and experimental validation of model systems for conformationally controlled reaction studies: separation of the conformers of 2,3-dibromobuta-1,3-diene in the gas phase, Physical Chemistry Chemical Physics 22, 13431–13439 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - G. F. von Rudorff, O. A. von Lilienfeld, Rapid and accurate molecular deprotonation energies from quantum alchemy, Physical Chemistry Chemical Physics 22, 10519–10525 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - P. D. Mezei, O. A. von Lilienfeld, Noncovalent Quantum Machine Learning Corrections to Density Functionals, Journal of Chemical Theory and Computation 16, 2647–2653 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - A. S. Christensen, L. A. Bratholm, F. A. Faber, O. A. von Lilienfeld, FCHL revisited: Faster and more accurate quantum machine learning, The Journal of Chemical Physics 152, 044107 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - J. Westermayr, F. A. Faber, A. S. Christensen, O. A. von Lilienfeld, P. Marquetand, Neural networks and kernel ridge regression for excited states dynamics of CH2NH2+: From single-state to multi-state representations and multi-property machine learning models, Machine Learning: Science and Technology 1, 025009 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - G. F. von Rudorff, O. A. von lilienfeld, Solving the inverse materials design problem with alchemical chirality, arXiv:2008.02784 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - B. Huang, O. A. von lilienfeld, Ab initio machine learning in chemical compound space, arXiv:2012.07502 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - O. Çaylak, O. A. von lilienfeld, B. Baumeier, Wasserstein metric for improved quantum machine learning with adjacency matrix representations, Machine Learning: Science and Technology 1, 03LT01 (2020). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - A. S. Christensen, O. A. von Lilienfeld, Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties, CHIMIA 73, 1028 (2019). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - G. F. von Rudorff, O. A. von Lilienfeld, Atoms in Molecules from Alchemical Perturbation Density Functional Theory, The Journal of Physical Chemistry B 123, 10073–10082 (2019). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - L. Cheng, M. Welborn, A. S. Christensen, T. F. Miller III, A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules, The Journal of Chemical Physics 150, 131103 (2019). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - P. Zaspel, B. Huang, H. Harbrecht, O. A. von Lilienfeld, Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited, Journal of Chemical Theory and Computation 15, 1546–1559 (2019). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - S. M. Keller, M. Samarin, M. Wieser, V. Roth, Deep Archetypal Analysis, 11824, 171 (2019). [Open Access URL]
Group(s): Roth / Project(s): INC2 - A. Kortylewski, A. Wieczorek, M. Wieser, C. Blumer, S. Parbhoo, A. Morel-Forster, V. Roth, T. Vetter, Greedy Structure Learning of Hierarchical Compositional Models, 11604 (2019). [Open Access URL]
Group(s): Roth / Project(s): INC2 - F. A. Faber, O. A. von Lilienfeld, Modeling Materials Quantum Properties with Machine Learning, 171–179 (2019).
Group(s): von Lilienfeld / Project(s): INC2 - A. S. Christensen, F. A. Faber, O. A. von Lilienfeld, Operators in quantum machine learning: Response properties in chemical space, The Journal of Chemical Physics 150, 064105 (2019). [Open Access URL]
Dataset on Figshare.
Group(s): von Lilienfeld / Project(s): INC2 - S. Fias, K. Y. S. Chang, O. A. von Lilienfeld, Alchemical Normal Modes Unify Chemical Space, The Journal of Physical Chemistry Letters 10, 30 (2019). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - A. Kortylewski, M. Wieser, A. Morel-Forster, A. Wieczore, S. Parbhoo, V. Roth, T. Vetter, Informed MCMC with Bayesian Neural Networks for Facial Image Analysis, (2018). [Open Access URL]
Group(s): Roth / Project(s): INC2 - S. Parbhoo, M. Wieser, V. Roth, Cause-Effect Deep Information Bottleneck for Incomplete Covariates, (2018). [Open Access URL]
Group(s): Roth / Project(s): INC2 - S. Parbhoo, M. Wieser, V. Roth, Estimating Causal Effects With Partial Covariates For Clinical Interpretability, (2018). [Open Access URL]
Group(s): Roth / Project(s): INC2 - F. A. Faber, A. S. Christensen, B. Huang, O. A. von Lilienfeld, Alchemical and structural distribution based representation for universal quantum machine learning, The Journal of Chemical Physics 148, 241717 (2018). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - B. Meyer, B. Sawatlon, S. Heinen, O. A. von Lilienfeld, C. Corminboeuf, Machine learning meets volcano plots: computational discovery of cross-coupling catalysts, Chemical Science 9, 7069 (2018). [Open Access URL]
Dataset on Materials Cloud.
Group(s): Corminboeuf, von Lilienfeld / Project(s): DD1, INC2 - K. Y. S. Chang, O. A. von Lilienfeld, AlxGa1-xAs crystals with direct 2 eV band gaps from computational alchemy, Physical Review Materials 2, 073802 (2018). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - M. Rupp, O. A. von Lilienfeld, K. Burke, Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry, The Journal of Chemical Physics 148, 241401 (2018). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - T. Bereau, R. A. D. Jr., A. Tkatchenko, O. A. von Lilienfeld, Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning, The Journal of Chemical Physics 148, 241706 (2018). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - O. A. von Lilienfeld, Quantum Machine Learning in Chemical Compound Space, Angewandte Chemie International Edition 57, 4164–4169 (2018).
Group(s): von Lilienfeld / Project(s): INC2 - J. J. Kranz, M. Kubillus, R. Ramakrishnan, O. A. von Lilienfeld, M. Elstner, Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning, Journal of Chemical Theory and Computation 14, 2341–2352 (2018).
Group(s): von Lilienfeld / Project(s): INC2 - D. N. Tahchieva, D. Bakowies, R. Ramakrishnan, O. A. von Lilienfeld, Torsional Potentials of Glyoxal, Oxalyl Halides, and Their Thiocarbonyl Derivatives: Challenges for Popular Density Functional Approximations, Journal of Chemical Theory and Computation 14, 4806–4817 (2018). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2 - B. Huang, N. O. Symonds, O. A. von Lilienfeld, Quantum Machine Learning in Chemistry and Materials, 1–27 (2018). [Open Access URL]
Group(s): von Lilienfeld / Project(s): INC2