*Organizers:* José Miguel Hernández-Lobato, Klaus-Robert Müller, Brooks Paige, Matt J. Kusner, Stefan Chmiela, Kristof T. Schütt

The success of machine learning has been demonstrated time and time again in classification, generative modelling, and reinforcement learning. This revolution in machine learning has largely been in domains with at least one of two key properties: (1) the input space is continuous, and thus classifiers and generative models are able to smoothly model unseen data that is ‘similar’ to the training distribution, or (2) it is trivial to generate data, such as in controlled reinforcement learning settings such as Atari or Go games, where agents can re-play the game millions of times. Unfortunately there are many important learning problems in chemistry, physics, materials science, and biology that do not share these attractive properties, problems where the input is molecular or material data.

Accurate prediction of atomistic 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 the prediction of atomistic properties as well as compound design and crystal 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 and material data [1-39]. 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 system and increased the efficiency of density 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.

08:30 | Opening remarks |
Brooks Paige | |

08:40 | Invited talk | Boltzmann Generators – Sampling Equilibrium States of Many-Body Systems with Deep Learning | Frank Noé |

09:00 | Invited talk | Deep Generative Models for Knowledge-Free Molecular Geometry | Kyunghyun Cho |

09:20 | Contributed talk | Band gap prediction for large organic crystal structures with machine learning | |

09:30 | Contributed talk | Uncertainty quantification of molecular property prediction using Bayesian neural network models | |

09:40 | Contributed talk | Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks | |

09:50 | Contributed talk | Incomplete Conditional Density Estimation for Fast Materials Discovery | |

10:00 | Poster session |
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10:30 | Coffee break | ||

11:00 | Invited talk | Generative deep models for predicting the effects of mutations | John Ingraham |

11:20 | Invited talk | Tensor Field Networks: rotation-, translation-, and permutation-equivariant convolutional NNs for 3D points | Tess Smidt |

11:40 | Invited talk | Deep Reinforcement Learning for de-novo Drug Design | Olexandr Isayev |

12:00 | Lunch |
||

14:00 | Invited talk | A translation approach to molecular graph optimization | Wengong Jin |

14:20 | Invited Talk | Predicting Electron-Ionization Mass Spectrometry using Neural Networks | Jennifer Wei |

14:40 | Invited talk | Statistical Perspective on Chemical Space with Quantum Mechanics and Machine Learning | Alexandre Tkatchenko |

15:00 | Coffee break | ||

15:30 | Invited talk | Application of graph neural networks in molecule design | Alex Gaunt |

15:50 | Invited talk | Design of Coarse-grained Molecular Models with Machine Learning | Cecilia Clementi |

16:10 | Invited talk | Covariant neural network architectures for learning physics | Risi Kondor |

16:30 | Contributed talk | Learning protein structure with a differentiable simulator | |

16:40 | Contributed talk | Generating equilibrium molecules with deep neural networks | |

16:50 | Contributed talk | Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation | |

17:00 | Contributed talk | Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction | |

17:10 | Closing remarks |
Brooks Paige | |

17:20 | Poster session |

- Graph-Based Network using Attention Mechanism for Predicting Molecular Properties
- Amir H. K. Ahmadi, Parsa Moradi, Babak H. Khalaj

- Efficient prediction of 3D electron densities using machine learning [arXiv]
- Mihail Bogojeski, Felix Brockherde, Leslie Vogt-Maranto, Li Li, Mark E. Tuckerman, Kieron Burke, Klaus-Robert Müller

- Spotlight Talk Generating equilibrium molecules with deep neural networks [arXiv]
- Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt

- Spotlight Talk Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks [arXiv]
- Clyde Fare, Lukas Turcani, Edward O. Pyzer-Knapp

- Spotlight Talk Incomplete Conditional Density Estimation for Fast Materials Discovery [GitHub]
- Phuoc Nguyen, Truyen Tran, Sunil Gupta, Santu Rana, Svetha Venkatesh

- Spotlight Talk Learning protein structure with a differentiable simulator
- John Ingraham, Adam Riesselman, Chris Sander, Debora Marks

- Spotlight Talk Predicting Electron-Ionization Mass Spectrometry using Neural Networks
- Jennifer N. Wei, David Belanger, Ryan P. Adams, D. Sculley

- Spotlight Talk Band gap prediction for large organic crystal structures with machine learning [arXiv]
- Bart Olsthoorn, R. Matthias Geilhufe, Stanislav S. Borysov, Alexander V. Balatsky

- Spotlight Talk Uncertainty quantification of molecular property prediction using Bayesian neural network models
- Seongok Ryu, Yongchan Kwon, Woo Youn Kim

- Batched Stochastic Bayesian Optimization via Combinatorial Constraints Design
- Kevin K. Yang, Yuxin Chen, Alycia Lee, Yisong Yue

- Spotlight Talk Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction [arXiv]
- Xavier Brumwell, Paul Sinz, Kwang Jin Kim, Yue Qi, Matthew Hirn

- Chemical Structure Elucidation from Mass Spectrometry by Matching Substructures [arXiv]
- Jing Lim, Joshua Wong, Minn Xuan Wong, Lee Han Eric Tan, Hai Leong Chieu, Davin Choo, Neng Kai Nigel Neo

- Design by Adaptive Sampling
- David H. Brookes, Jennifer Listgarten

- Spotlight Talk Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation [chemRxiv]
- Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee

- Descriptor for Separating Base-material and Additive in Machine Learning of Thermoelectric Material Property Prediction
- Reiko Hagawa, Hiromasa Tamaki, Koji Morikawa

- Convolutional models of RNA energetics [bioRxiv]
- Michelle J. Wu

- PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Molecules [arXiv]
- Payel Das, Kahini Wadhawan, Oscar Chang, Tom Sercu, Cicero dos Santos, Matthew Riemer, Vijil Chenthamarakshan, Inkit Padhi, Aleksandra Mojsilovic

- Transferrable End-to-End Learning for Protein Interface Prediction [arXiv]
- Raphael J. L. Townshend, Rishi Bedi, Ron O. Dror

- Pre-training Graph Neural Networks with Kernels [arXiv]
- Nicolò Navarin, Dinh V. Tran, Alessandro Sperduti

- DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation [arXiv]
- Rim Assouel, Mohamed Ahmed, Marwin H. Segler, Amir Saffari, Yoshua Bengio

- TorchProteinLibrary: A computationally efficient, differentiable representation of protein structure
- Georgy Derevyanko, Guillaume Lamoureux

- CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations [arXiv]
- Arindam Paul, Dipendra Jha, Reda Al-Bahrani, Wei-keng Liao, Alok Choudhary, Ankit Agrawal

- Generative Modeling for Multimodal Structure-Based Drug Design
- Miha Skalic, Davide Sabbadin, Gianni De Fabritiis

- N-Gram Graph, A Novel Molecule Representation [GitHub]
- Shengchao Liu, Thevaa Chandereng, Yingyu Liang

- Neural Reasoning for Chemical-Chemical Interaction [GitHub]
- Trang Pham, Truyen Tran, Svetha Venkatesh

- MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction [arXiv]
- Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek Kumar, Padmini Rajagopalan, Suchismita Sanyal, Partha Talukdar

- Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design [chemRxiv]
- Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee

- Bayesian Optimization of High Transparency, Low Haze, and High Oil Contact Angle Rigid and Flexible Optoelectronic Substrates
- Sajad Haghanifar, Sooraj Sharma, Luke M. Tomasovic, and Paul. W. Leu, Bolong Cheng

- Fast classification of small X-ray diffraction datasets using physics-based data augmentation and deep neural networks
- Felipe Oviedo, Zekun Ren, Shijing Sun, Charlie Settens, Zhe Liu, Giuseppe Romano, Tonio Buonassisi, Ramasamy Savitha, Siyu I.P. Tian, Brian L. DeCost, Aaron Gilad Kusne

- PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks [arXiv]
- Ali Oskooei, Jannis Born, Matteo Manica, Vigneshwari Subramanian, Julio Sáez-Rodríguez, María Rodríguez Martínez

- Multiple-objective Reinforcement Learning for Inverse Design and Identification
- Haoran Wei, Mariefel Olarte, Garrett B. Goh

- Efficient nonmyopic active search with applications in drug and materials discovery [arXiv]
- Shali Jiang, Gustavo Malkomes, Benjamin Moseley, Roman Garnett

- Inference of the three-dimensional chromatin structure and its temporal behavior [arXiv]
- Bianca-Cristina Cristescu, Zalán Borsos, John Lygeros, María Rodríguez Martínez, Maria Anna Rapsomaniki

- Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials [arXiv]
- Peter Bjørn Jørgensen, Karsten Wedel Jacobsen, Mikkel N. Schmidt

- Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning [arXiv]
- Zois Boukouvalas, Daniel C. Elton, Peter W. Chung, Mark D. Fuge

- Optimizing Interface/Surface Roughness for Thermal Transport
- Shenghong Ju, Thaer M. Dieb, Koji Tsuda, Junichiro Shiomi

- Graph Convolutional Neural Networks for Polymers Property Prediction [arXiv]
- Minggang Zeng, Jatin Nitin Kumar, Zeng Zeng, Ramasamy Savitha, Vijay Ramaseshan Chandrasekhar, Kedar Hippalgaonkar

- Generative Model for Material Experiments Based on Prior Knowledge and Attention Mechanism [arXiv]
- Mincong Luo, X. He, Li Liu

- Physics-aware Deep Generative Models for Creating Synthetic Microstructures [arXiv]
- Rahul Singh, Viraj Shah, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, Chinmay Hegde

- Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks [arXiv]
- Leonardo Banchi, Edward Grant, Andrea Rocchetto, Simone Severini

- Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes [arXiv]
- Jialin Song, Yury S. Tokpanov, Yuxin Chen, Dagny Fleischman, Kate T. Fountaine, Harry A. Atwater, Yisong Yue

- Interpretable deep learning for guided structure-property explorations in photovoltaics [arXiv]
- Balaji Sesha Sarath Pokuri, Sambuddha Ghosal, Apurva Kokate, Baskar Ganapathysubramanian, Soumik Sarkar

- Analysis of Atomistic Representations Using Weighted Skip-Connections [arXiv]
- Kim A. Nicoli, Pan Kessel, Michael Gastegger, Kristof T. Schütt

- Predicting thermoelectric properties from crystal graphs and material descriptors – first application for functional materials [arXiv]
- Leo Laugier, Daniil Bash, Jose Recatala, Hong Kuan Ng, Savitha Ramasamy, Chuan-Sheng Foo, Vijay R. Chandrasekhar, Kedar Hippalgaonkar

- 3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys [arXiv]
- Rahul Singh, Aayush Sharma, Onur Rauf Bingol, Aditya Balu, Ganesh Balasubramanian, Duane D. Johnson, Soumik Sarkar

- High Quality Protein Q8 Secondary Structure Prediction by Diverse Neural Network Architectures [arXiv]
- Iddo Drori, Isht Dwivedi, Pranav Shrestha, Jeffrey Wan, Yueqi Wang, Yunchu He, Anthony Mazza, Hugh Krogh-Freeman, Dimitri Leggas, Kendal Sandridge, Linyong Nan, Kaveri Thakoor, Chinmay Joshi, Sonam Goenka, Chen Keasar, Itsik Pe’er

- Deep Learning and Density Functional Theory
- Kevin Ryczko, David Strubbe, Isaac Tamblyn

- Discovering and Fusing Relations in Molecules with Spectral Graph Networks [arXiv]
- Boris Knyazev, Xiao Lin, Mohamed R. Amer, Graham W. Taylor

- Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction
- Wei-Cheng Tseng, Po-Han Chi, Jia-Hua Wu, Min Sun

Please direct any questions to nips2018moleculesworkshop@gmail.com.

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