SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

26 June 2017

Kristof T. Schütt

Pieter-Jan Kindermans

Huziel Enoc Sauceda Felix

Papers citing "SchNet: A continuous-filter convolutional neural network for modeling quantum interactions"

37 / 187 papers shown

Title
OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features Zhuoran Qiao Matthew Welborn Anima Anandkumar F. Manby Thomas F. Miller AI4CE 22 214 0 15 Jul 2020
Improving neural network predictions of material properties with limited data using transfer learning Schuyler Krawczuk D. Venturi AI4CE 6 3 0 29 Jun 2020
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes Ryan-Rhys Griffiths Jake L. Greenfield Aditya R. Thawani Arian R. Jamasb Henry B. Moss Anthony Bourached Penelope Jones William McCorkindale Alexander A. Aldrick Matthew J. Fuchter Alpha A. Lee 22 13 0 28 Jun 2020
Hierarchical Inter-Message Passing for Learning on Molecular Graphs Matthias Fey Jan-Gin Yuen F. Weichert GNN 33 86 0 22 Jun 2020
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks F. Fuchs Daniel E. Worrall Volker Fischer Max Welling 3DPC 45 665 0 18 Jun 2020
Self-Supervised Graph Transformer on Large-Scale Molecular Data Yu Rong Yatao Bian Tingyang Xu Wei-yang Xie Ying Wei Wenbing Huang Junzhou Huang AI4CE 13 25 0 18 Jun 2020
Higher-Order Explanations of Graph Neural Networks via Relevant Walks Thomas Schnake Oliver Eberle Jonas Lederer Shinichi Nakajima Kristof T. Schütt Klaus-Robert Muller G. Montavon 32 215 0 05 Jun 2020
Non-Local Graph Neural Networks Meng Liu Zhengyang Wang Shuiwang Ji 24 161 0 29 May 2020
NENET: An Edge Learnable Network for Link Prediction in Scene Text M. Singh Sayan Banerjee S. Chaudhuri 21 1 0 25 May 2020
A deep neural network for molecular wave functions in quasi-atomic minimal basis representation M. Gastegger A. McSloy M. Luya Kristof T. Schütt R. Maurer 11 46 0 11 May 2020
Autonomous discovery in the chemical sciences part II: Outlook Connor W. Coley Natalie S. Eyke K. Jensen 21 171 0 30 Mar 2020
Directional Message Passing for Molecular Graphs Johannes Klicpera Janek Groß Stephan Günnemann 49 846 0 06 Mar 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 103 49 0 27 Feb 2020
Neural Message Passing on High Order Paths Daniel Flam-Shepherd Tony C Wu Pascal Friederich Alán Aspuru-Guzik GNN AI4CE 21 49 0 24 Feb 2020
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics G. Simm Robert Pinsler José Miguel Hernández-Lobato AI4CE 8 82 0 18 Feb 2020
Hierarchical Generation of Molecular Graphs using Structural Motifs Wengong Jin Regina Barzilay Tommi Jaakkola 21 279 0 08 Feb 2020
Quaternion Equivariant Capsule Networks for 3D Point Clouds Yongheng Zhao Tolga Birdal J. E. Lenssen Emanuele Menegatti Leonidas J. Guibas Federico Tombari 3DPC 26 88 0 27 Dec 2019
Generating valid Euclidean distance matrices Moritz Hoffmann Frank Noé 19 56 0 07 Oct 2019
A Generative Model for Molecular Distance Geometry G. Simm José Miguel Hernández-Lobato GAN 21 107 0 25 Sep 2019
Deformable Filter Convolution for Point Cloud Reasoning Yuwen Xiong Mengye Ren Renjie Liao K. Wong R. Urtasun 3DPC 31 17 0 30 Jul 2019
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective Chengqiang Lu Qi Liu Chao Wang Zhenya Huang Peize Lin Lixin He AI4CE 11 191 0 25 Jun 2019
Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions Kristof T. Schütt M. Gastegger A. Tkatchenko K. Müller R. Maurer AI4CE 29 381 0 24 Jun 2019
Cormorant: Covariant Molecular Neural Networks Brandon M. Anderson Truong Son-Hy Risi Kondor 19 421 0 06 Jun 2019
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules Niklas W. A. Gebauer M. Gastegger Kristof T. Schütt 23 201 0 02 Jun 2019
Software and application patterns for explanation methods Maximilian Alber 25 11 0 09 Apr 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 17 996 0 26 Feb 2019
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization Wengong Jin Kevin Kaichuang Yang Regina Barzilay Tommi Jaakkola 33 224 0 03 Dec 2018
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation Linfeng Zhang De-Ye Lin Han Wang R. Car E. Weinan 9 326 0 28 Oct 2018
Generating equilibrium molecules with deep neural networks Niklas W. A. Gebauer M. Gastegger Kristof T. Schütt BDL 17 38 0 26 Oct 2018
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris Martin Ritzert Matthias Fey William L. Hamilton J. E. Lenssen Gaurav Rattan Martin Grohe GNN 67 1,605 0 04 Oct 2018
Quantum-chemical insights from interpretable atomistic neural networks Kristof T. Schütt M. Gastegger A. Tkatchenko K. Müller AI4CE 23 31 0 27 Jun 2018
Hierarchical Graph Representation Learning with Differentiable Pooling Rex Ying Jiaxuan You Christopher Morris Xiang Ren William L. Hamilton J. Leskovec GNN 54 2,124 0 22 Jun 2018
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials Peter Bjørn Jørgensen K. Jacobsen Mikkel N. Schmidt 30 82 0 08 Jun 2018
Visualizing Convolutional Neural Network Protein-Ligand Scoring Joshua E. Hochuli Alec Helbling Tamar Skaist Matthew Ragoza D. Koes FAtt 11 65 0 06 Mar 2018
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds Nathaniel Thomas Tess E. Smidt S. Kearnes Lusann Yang Li Li Kai Kohlhoff Patrick F. Riley 3DPC 22 940 0 22 Feb 2018
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels Matthias Fey J. E. Lenssen F. Weichert H. Müller 3DPC 28 437 0 24 Nov 2017
Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks Matthew Ragoza Lillian Turner D. Koes 22 16 0 20 Oct 2017