Machine Learning for Molecules and Materials
Kristof Schütt · Klaus-Robert Müller · Anatole von Lilienfeld · José Miguel Hernández-Lobato · Klaus-Robert Müller · Alan Aspuru-Guzik · Bharath Ramsundar · Matt Kusner · Brooks Paige · Stefan Chmiela · Alexandre Tkatchenko · Anatole von Lilienfeld · Koji Tsuda

Fri Dec 8th 08:00 AM -- 06:30 PM @ S4
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The success of machine learning has been demonstrated time and time again in classification, generative modelling, and reinforcement learning. In particular, we have recently seen interesting developments where ML has been applied to the natural sciences (chemistry, physics, materials science, neuroscience and biology). Here, often the data is not abundant and very costly. This workshop will focus on the unique challenges of applying machine learning to molecules and materials.

Accurate prediction of chemical and physical properties is a crucial ingredient toward rational compound design in chemical and pharmaceutical industries. Many discoveries in chemistry can be guided by screening large databases of computational molecular structures and properties, but high level quantum-chemical calculations can take up to several days per molecule or material at the required accuracy, placing the ultimate achievement of in silico design out of reach for the foreseeable future. In large part the current state of the art for such problems is the expertise of individual researchers or at best highly-specific rule-based heuristic systems. Efficient methods in machine learning, applied to property and structure prediction, can therefore have pivotal impact in enabling chemical discovery and foster fundamental insights.

Because of this, in the past few years there has been a flurry of recent work towards designing machine learning techniques for molecule [1, 2, 4-11, 13-18, 20, 21, 23-32, 34-38] and material data [1-3, 5, 6, 12, 19, 24, 33]. These works have drawn inspiration from and made significant contributions to areas of machine learning as diverse as learning on graphs to models in natural language processing. Recent advances enabled the acceleration of molecular dynamics simulations, contributed to a better understanding of interactions within quantum many-body systems and increased the efficiency of density functional theory based quantum mechanical modeling methods. This young field offers unique opportunities for machine learning researchers and practitioners, as it presents a wide spectrum of challenges and open questions, including but not limited to representations of physical systems, physically constrained models, manifold learning, interpretability, model bias, and causality.

The goal of this workshop is to bring together researchers and industrial practitioners in the fields of computer science, chemistry, physics, materials science, and biology all working to innovate and apply machine learning to tackle the challenges involving molecules and materials. In a highly interactive format, we will outline the current frontiers and present emerging research directions. We aim to use this workshop as an opportunity to establish a common language between all communities, to actively discuss new research problems, and also to collect datasets by which novel machine learning models can be benchmarked. The program is a collection of invited talks, alongside contributed posters. A panel discussion will provide different perspectives and experiences of influential researchers from both fields and also engage open participant conversation. An expected outcome of this workshop is the interdisciplinary exchange of ideas and initiation of collaboration.

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[8] Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R., Burke, K. (2012). Finding density functionals with machine learning. Phys. Rev. Lett., 108(25), 253002.
[9] Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.-R., von Lilienfeld, O. A. (2013). Machine learning of molecular electronic properties in chemical compound space. New J. Phys., 15(9), 095003.
[10] Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., Tkatchenko, A., Müller, K.-R. (2013). Assessment and validation of machine learning methods for predicting molecular atomization energies. J. Chem. Theory Comput., 9(8), 3404-3419.
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[16] Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., Tkatchenko, A. (2015). Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett, 6(12), 2326-2331.
[17] Alipanahi, B., Delong, A., Weirauch, M. T., Frey, B. J. (2015). Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. ‎Nat. Biotechnol., 33(8), 831-838.
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[19] Faber F. A., Lindmaa A., von Lilienfeld, O. A., Armiento, R. (2016). Machine learning energies of 2 million elpasolite (A B C 2 D 6) crystals. Phys. Rev. Lett., 117(13), 135502.
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[21] Wei, J. N., Duvenaud, D, Aspuru-Guzik, A. (2016). Neural networks for the prediction of organic chemistry reactions. ACS Cent. Sci., 2(10), 725-732.
[22] Sadowski, P., Fooshee, D., Subrahmanya, N., Baldi, P. (2016). Synergies between quantum mechanics and machine learning in reaction prediction. J. Chem. Inf. Model., 56(11), 2125-2128.
[23] Lee, A. A., Brenner, M. P., Colwell L. J. (2016). Predicting protein-ligand affinity with a random matrix framework. Proc. Natl. Acad. Sci., 113(48), 13564-13569.
[24] Behler, J. (2016). Perspective: Machine learning potentials for atomistic simulations. J. Chem. Phys., 145(17), 170901.
[25] De, S., Bartók, A. P., Csányi, G., Ceriotti, M. (2016). Comparing molecules and solids across structural and alchemical space. Phys. Chem. Chem. Phys., 18(20), 13754-13769.
[26] Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K.-R., Tkatchenko, A. (2017). Quantum-chemical insights from deep tensor neural networks. Nat. Commun., 8, 13890.
[27] Segler, M. H., Waller, M. P. (2017). Neural‐symbolic machine learning for retrosynthesis and reaction prediction. ‎Chem. Eur. J., 23(25), 5966-5971.
[28] Kusner, M. J., Paige, B., Hernández-Lobato, J. M. (2017). Grammar variational autoencoder. arXiv preprint arXiv:1703.01925.
[29] Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H., Jensen K. F. (2017). Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci., 3(5), 434-443.
[30] Altae-Tran, H., Ramsundar, B., Pappu, A. S., Pande, V. (2017). Low data drug discovery with one-shot learning. ACS Cent. Sci., 3(4), 283-293.
[31] Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., Dahl, G. E. (2017). Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212.
[32] Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, Igor, Schütt, K. T., Müller, K.-R. (2017). Machine learning of accurate energy-conserving molecular force fields. Sci. Adv., 3(5), e1603015.
[33] Ju, S., Shiga T., Feng L., Hou Z., Tsuda, K., Shiomi J. (2017). Designing nanostructures for phonon transport via bayesian optimization. Phys. Rev. X, 7(2), 021024.
[34] Ramakrishnan, R, von Lilienfeld, A. (2017). Machine learning, quantum chemistry, and chemical space. Reviews in Computational Chemistry, 225-256.
[35] Hernandez-Lobato, J. M., Requeima, J., Pyzer-Knapp, E. O., Aspuru-Guzik, A. (2017). Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space. arXiv preprint arXiv:1706.01825.
[36] Smith, J., Isayev, O., Roitberg, A. E. (2017). ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci., 8(4), 3192-3203.
[37] Brockherde, F., Li, L., Burke, K., Müller, K.-R. By-passing the Kohn-Sham equations with machine learning. Nat. Commun., in press.
[38] Schütt, K. T., Kindermans, P. J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., Müller, K. R. (2017). MolecuLeNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS (accepted).

08:00 AM Opening remarks <span> <a href="#"></a> </span> Klaus-Robert Müller
08:20 AM Machine Learning for Molecular Materials Design (Invited talk) Alan Aspuru-Guzik
08:45 AM TBA4 (Invited talk) Robert DiStasio Jr.
09:00 AM New Density Functionals Created by Machine Learning (Invited talk) Kieron burke
09:35 AM Poster spotlights <span> <a href="#"></a> </span> Emma Strubell, Garrett Goh, Masashi Tsubaki, Théophile Gaudin, Philippe Schwaller, Matt Ragoza, Rafael Gomez-Bombarelli
10:45 AM Quantum Machine Learning (Invited talk) Anatole von Lilienfeld
11:05 AM Machine Learning in Organic Synthesis Planning And Execution (Invited talk) Klavs Jensen
11:25 AM Neural-network Quantum States (Invited talk) Giuseppe Carleo
11:40 AM Quantitative Attribution: Do Neural Network Models Learn the Correct Chemistry? (Invited talk) Lucy Colwell
01:55 PM TBA11 (Invited talk) Alex Smola
02:00 PM ChemTS: An Efficient Python Library for De Novo Molecular Generation (Invited talk) Koji Tsuda
02:15 PM Symmetry Matters: Learning Scalars and Tensors in Materials and Molecules (Invited talk) Michele Ceriotti
02:35 PM Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields (Short) Stefan Chmiela
03:25 PM N-body Neural Networks: A General Compositional Architecture For Representing Multiscale Physical Systems (Invited talk) Risi Kondor
03:45 PM Distilling Expensive Simulations with Neural Networks (Invited talk) Oriol Vinyals
04:05 PM Automatic Chemical Design Using a Data-driven Continuous Representation of Molecules (Invited talk) David Duvenaud
04:20 PM Planning Chemical Syntheses with Neural Networks and Monte Carlo Tree Search (Invited talk) Marwin Segler
05:50 PM Closing remarks <span> <a href="#"></a> </span> Jose Miguel Hernández-Lobato

Author Information

Kristof Schütt (TU Berlin)
Klaus-Robert Müller (TU Berlin)
Anatole von Lilienfeld (Universität Basel)
Jose Miguel Hernández-Lobato (University of Cambridge)
Klaus-Robert Müller (TU Berlin)
Alan Aspuru-Guzik (Harvard University)
Bharath Ramsundar (Stanford)
Matt Kusner (University of Oxford)
Brooks Paige (Alan Turing Institute / University of Cambridge)
Stefan Chmiela (Technische Universität Berlin)
Alexandre Tkatchenko (University of Luxembourg)
Anatole von Lilienfeld (University of Basel)
Koji Tsuda (The University of Tokyo / RIKEN)

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