Room S4, Long Beach Convention Center


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.


08:00 Opening Remarks Klaus-Robert Müller
Introduction to Machine Learning and Chemistry
08:20 Invited Talk Machine Learning for Molecular Materials Design Alán Aspuru-Guzik
08:45 Invited Talk [TBA] Robert A. DiStasio Jr.
09:00 Invited Talk New Density Functionals Created by Machine Learning Kieron Burke
09:25 Q/A Session
09:35 Poster Spotlights
10:15 Poster Session Coffee
Machine Learning Applications in Chemistry
10:45 Invited Talk Quantum Machine Learning O. Anatole von Lilienfeld
11:05 Invited Talk Machine Learning in Organic Synthesis Planning And Execution Klavs F. Jensen
11:25 Invited Talk Neural-network Quantum States Giuseppe Carleo
11:40 Invited Talk Quantitative Attribution: Do Neural Network Models Learn the Correct Chemistry? Lucy Colwell
11:55 Q/A Session
12:05 Lunch
Kernel Learning with Structured Data
13:35 Invited Talk Differentiable System Learning Alexander J. Smola
14:00 Invited talk ChemTS: An Efficient Python Library for De Novo Molecular Generation Koji Tsuda
14:15 Invited Talk Symmetry Matters: Learning Scalars and Tensors in Materials and Molecules Michele Ceriotti
14:35 Short Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields Stefan Chmiela
14:45 Q/A Session
14:55 Poster Session Coffee
Deep Learning Approaches
15:25 Invited Talk N-body Neural Networks: A General Compositional Architecture For Representing Multiscale Physical Systems Risi Kondor
15:45 Invited Talk Distilling Expensive Simulations with Neural Networks Oriol Vinyals
16:05 Invited Talk Automatic Chemical Design Using a Data-driven Continuous Representation of Molecules David Duvenaud
16:20 Invited Talk Planning Chemical Syntheses with Neural Networks and Monte Carlo Tree Search Marwin Segler
16:40 Q/A Session
16:50 Panel Discussion
17:20 Closing Remarks José Miguel Hernández-Lobato
17:35 Poster Session

Accepted Papers

A Chemical Bond-based Representation of Materials [arXiv]
Van-Doan Nguyen, Le Dinh Khiet, Pham Tien Lam, Dam Hieu Chi
Spotlight Talk Automatically Extracting Action Graphs From Materials Science Synthesis Procedures [arXiv]
Sheshera Mysore, Edward Kim, Emma Strubell, Ao Liu, Haw-Shiuan Chang, Srikrishna Kompella, Kevin Huang, Andrew McCallum, Elsa Olivetti
Bayesian Protein Optimization
Stephan Eismann, Karen Sarkisyan, Stefano Ermon
Calibrated Boosting-forest [arXiv]
Haozhen Wu
Spotlight Talk ChemNet: A Transferable and Generalizable Deep Neural Network for Small-molecule Property Prediction
Garrett B. Goh, Charles Siegel, Abhinav Vishnu, Nathan Hodas
Constrained Bayesian Optimization for Automatic Chemical Design [arXiv]
Ryan-Rhys Griffiths, José Miguel Hernández-Lobato
Deep Learning for Prediction of Synergistic Effects of Anti-cancer Drugs
Kristina Preuer, Richard P.I. Lewis, Sepp Hochreiter, Andreas Bender, Krishna C. Bulusu, Günter Klambauer
Deep Learning Yields Virtual Assays
Thomas Unterthiner, Günter Klambauer, Andreas Mayr, Sepp Hochreiter
Spotlight Talk End-to-end Learning of Graph Neural Networks for Latent Molecular Representations
Masashi Tsubaki, Masashi Shimbo, Atsunori Kanemura, Hideki Asoh
Spotlight Talk “Found in translation”: Predicting Outcomes of Complex Organic Chemistry Reactions Using Neural Sequence-to-sequence Models [arXiv]
Philippe Schwaller, Théophile Gaudin, Dávid Lányi, Costas Bekas, Teodoro Laino
Spotlight Talk Learning a Generative Model for Validity in Complex Discrete Structures [arXiv]
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J. Kusner, José Miguel Hernández-Lobato
Learning Hard Quantum Distributions With Variational Autoencoders [arXiv]
Andrea Rocchetto, Edward Grant, Sergii Strelchuk, Giuseppe Carleo, Simone Severini
Spotlight Talk Ligand Pose Optimization With Atomic Grid-based Convolutional Neural Networks [arXiv]
Matthew Ragoza, Lillian Turner, David Ryan Koes
Machine Learning-enabled Study of Proton Transfer Reaction Mechanisms on Titania Surfaces
Qian Yang, Muralikrishna Raju, Matthias Ihme, Evan J. Reed
Neural Network for Learning Universal Atomic Forces
Pham Tien Lam, Hiori Kinob, Takashi Miyakeb, Nguyen Viet Cuong, Dam Hieu Chia
Overcoming Data Scarcity With Transfer Learning [arXiv]
Maxwell L. Hutchinson, Erin Antono, Brenna M. Gibbons, Sean Paradiso, Julia Ling, Bryce Meredig
Pure Density Functional for Strong Correlations and the Thermodynamic Limit From Machine Learning
Li Li, Thomas E. Baker, Steven R. White, Kieron Burke
Spotlight Talk Semi-supervised Continuous Representation of Molecules [arXiv]
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Timothy D. Hirzel, Jorge Aguilera-Iparraguirre, Ryan P. Adams, Alán Aspuru-Guzik
Semi-supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing [arXiv]
Hai Nguyen, Shin-ichi Maeda, Kenta Oono
Spotlight Talk Syntax-directed Variational Autoencoder for Molecule Generation [PDF]
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, Le Song
Toxicity Prediction Using Self-normalizing Networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter
Unsupervised Learning of Dynamical and Molecular Similarity Using Variance Minimization
Brooke E. Husic, Vijay S. Pande


The Alan Turing Institute

The Alan Turing Institute

The Alan Turing Institute is the national institute for data science, headquartered at the British Library. Our mission is to make great leaps in data science research in order to change the world for the better.

Research excellence is the foundation of the Institute: the sharpest minds from the data science community investigating the hardest questions. We work with integrity and dedication. Our researchers collaborate across disciplines to generate impact, both through theoretical development and application to real-world problems. We are fuelled by the desire to innovate and add value.

Data science will change the world. We are pioneers; training the next generation of data science leaders, shaping the public conversation, and pushing the boundaries of this new science for the public good.


BenevolentAI: Artificial Intelligence for Scientific Innovation

BenevolentAI has built a leading position in artificial intelligence by developing technologies that deliver previously unimaginable scientific advances, rapidly accelerate scientific innovation and completely disrupt traditional methods of scientific discovery. The technology has been validated in drug discovery, specifically, in the most challenging field of human biology: the identification of new disease targets.

By amplifying a researchers’ ability to grasp an entire corpus of data and iterate the scientific method at exponentially faster rates, BenevolentAI brings highly advanced tools to traditional R&D programmes enabling artificial intelligence to be applied to the scientific discovery process. In just 2 years, the Company has developed a pipeline of twenty-two pre-clinical and clinical drug programmes, a process normally taking 10 to 15 years.

BenevolentAI is hiring Machine Learning Researchers expert in NLP, Reinforcement Learning or Chemistry Machine Learning to join its New York and London Offices

If you are interested visit our website | Contact us careers@benevolent.ai | Follow us @benevolent_ai

Technische Universität Berlin

Technische Universität Berlin

With around 32000 students, circa 100 course offerings and 40 Institutes, the historic Technische Universität Berlin is one of Germany’s largest and most internationally renowned technical universities. Located in Germany’s capital city – at the heart of Europe – outstanding achievements in research and teaching, imparting skills to excellent graduates, and a modern service-oriented administration characterize TU Berlin.

With six institutes, 60 professors and more than 500 scientific staff members, Faculty IV is one of the leading university faculties of its kind in Germany. The Faculty’s scientific productivity is reflected both in the large number of publications and the high level of external funding. Our research collaboration with top universities in North America, Europe, and Asia ensures an ongoing international exchange of ideas and information. The numerous honors awarded to the Faculty’s scientists are yet another reason for our outstanding reputation.

Visit our website at http://www.ml.tu-berlin.de/menue/machine_learning/.


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Contact: Please direct any questions to qm.nips2017@gmail.com.