Machine learning for molecular simulation

7 November 2019

Frank Noé

Papers citing "Machine learning for molecular simulation"

45 / 45 papers shown

Title
JaxSGMC: Modular stochastic gradient MCMC in JAX Stephan Thaler Paul Fuchs Ana Cukarska Julija Zavadlav BDL 48 2 0 16 May 2025
A practical guide to machine learning interatomic potentials -- Status and future Ryan Jacobs D. Morgan Siamak Attarian Jun Meng Chen Shen ... K. J. Schmidt So Takamoto Aidan Thompson Julia Westermayr Brandon M. Wood 59 4 0 12 Mar 2025
Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies Vivin Vinod Peter Zaspel 26 0 0 15 Oct 2024
Foundation Inference Models for Markov Jump Processes David Berghaus K. Cvejoski Patrick Seifner C. Ojeda Ramses J. Sanchez 47 1 0 10 Jun 2024
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models Bálint Máté François Fleuret Tristan Bereau DiffM 46 2 0 04 Jun 2024
Morphological Symmetries in Robotics Daniel Felipe Ordoñez Apraez Giulio Turrisi Vladimir Kostic Mario Martin Antonio Agudo Francesc Moreno-Noguer Massimiliano Pontil Claudio Semini Carlos Mastalli AI4CE 42 6 0 23 Feb 2024
A new perspective on building efficient and expressive 3D equivariant graph neural networks Weitao Du Yuanqi Du Limei Wang Dieqiao Feng Guifeng Wang Shuiwang Ji Carla P. Gomes Zhixin Ma AI4CE 42 34 0 07 Apr 2023
Multilevel CNNs for Parametric PDEs Cosmas Heiß Ingo Gühring Martin Eigel AI4CE 25 8 0 01 Apr 2023
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space Ayana Ghosh Sergei V. Kalinin M. Ziatdinov 17 8 0 06 Jan 2023
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence Stefan Blücher Klaus-Robert Muller Stefan Chmiela 19 4 0 24 Dec 2022
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner Julija Zavadlav 40 21 0 15 Dec 2022
SchNetPack 2.0: A neural network toolbox for atomistic machine learning Kristof T. Schütt Stefaan S. P. Hessmann Niklas W. A. Gebauer Jonas Lederer M. Gastegger 29 59 0 11 Dec 2022
Equivariant Networks for Crystal Structures Sekouba Kaba Siamak Ravanbakhsh AI4CE 48 24 0 15 Nov 2022
Towards Learned Simulators for Cell Migration Koen Minartz Y. Poels Vlado Menkovski 37 1 0 02 Oct 2022
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields Niklas Schmitz Klaus-Robert Muller Stefan Chmiela AI4CE 29 11 0 25 Aug 2022
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity Nikhil V. S. Avula S. K. Veesam Sudarshan Behera S. Balasubramanian 24 8 0 23 Aug 2022
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems Qianxiao Li T. Lin Zuowei Shen 24 6 0 18 Aug 2022
Accelerating discrete dislocation dynamics simulations with graph neural networks N. Bertin Fei Zhou AI4CE 26 9 0 05 Aug 2022
Towards Learning Self-Organized Criticality of Rydberg Atoms using Graph Neural Networks Simon Ohler Daniel Brady Winfried Lotzsch M. Fleischhauer Johannes Otterbach AI4CE 30 1 0 05 Jul 2022
So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems J. Frank Oliver T. Unke Klaus-Robert Muller 27 43 0 28 May 2022
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations Oliver T. Unke M. Stohr Stefan Ganscha Thomas Unterthiner Hartmut Maennel ... Daniel Ahlin M. Gastegger L. M. Sandonas A. Tkatchenko Klaus-Robert Muller AI4CE 45 18 0 17 May 2022
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning Sajjad Heydari S. Raniolo L. Livi V. Limongelli 32 2 0 06 May 2022
DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations Fang Wu Stan Z. Li DiffM 37 31 0 19 Apr 2022
Automatic Identification of Chemical Moieties Jonas Lederer M. Gastegger Kristof T. Schütt Michael C. Kampffmeyer Klaus-Robert Muller Oliver T. Unke 28 5 0 30 Mar 2022
Interactive Visualization of Protein RINs using NetworKit in the Cloud Eugenio Angriman Fabian Brandt-Tumescheit Leon Franke Alexander van der Grinten Henning Meyerhenke MLAU 6 1 0 02 Mar 2022
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 36 63 0 21 Dec 2021
Graph Neural Networks Accelerated Molecular Dynamics Zijie Li Kazem Meidani Prakarsh Yadav A. Farimani GNN AI4CE 32 53 0 06 Dec 2021
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows P. D. Haan Corrado Rainone Miranda C. N. Cheng Roberto Bondesan AI4CE 16 35 0 06 Oct 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey Rui Wang Rose Yu AI4CE PINN 46 65 0 02 Jul 2021
BIGDML: Towards Exact Machine Learning Force Fields for Materials H. E. Sauceda Luis E Gálvez-González Stefan Chmiela L. O. Paz-Borbón K. Müller A. Tkatchenko AI4CE 34 47 0 08 Jun 2021
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting Stephan Thaler Julija Zavadlav 27 66 0 02 Jun 2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 177 248 0 01 May 2021
Using Deep LSD to build operators in GANs latent space with meaning in real space J. Q. Toledo-Marín J. Glazier GAN 22 3 0 09 Feb 2021
DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction Peter Bjørn Jørgensen Arghya Bhowmik 25 20 0 04 Nov 2020
Machine Learning Force Fields Oliver T. Unke Stefan Chmiela H. E. Sauceda M. Gastegger I. Poltavsky Kristof T. Schütt A. Tkatchenko K. Müller AI4CE 34 888 0 14 Oct 2020
Set Prediction without Imposing Structure as Conditional Density Estimation David W. Zhang Gertjan J. Burghouts Cees G. M. Snoek 51 17 0 08 Oct 2020
Deep Learning in Protein Structural Modeling and Design Wenhao Gao S. Mahajan Jeremias Sulam Jeffrey J. Gray 29 159 0 16 Jul 2020
Machine learning for electronically excited states of molecules Julia Westermayr P. Marquetand 25 258 0 10 Jul 2020
Conditional Set Generation with Transformers Adam R. Kosiorek Hyunjik Kim Danilo Jimenez Rezende 24 40 0 26 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 34 217 0 05 Jun 2020
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach Jiang Wang Stefan Chmiela K. Müller Frank Noè C. Clementi 8 46 0 04 May 2020
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 111 356 0 30 Oct 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 251 1,787 0 02 Mar 2017
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations Hao Wu Feliks Nuske Fabian Paul Stefan Klus P. Koltai Frank Noé 107 126 0 20 Oct 2016