Fetching the paper…
Reading the bibliography…
The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research.
Pytorch: An imperative style, high-performance deep learning library, 2019
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 1912
Earlier work this paper cites.
Directional message passing for molecular graphs, 2020a
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2003
Earlier work this paper cites.
Se(3)-transformers: 3d roto-translation equivariant attention networks, 2020
Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, and Max Welling · 2006
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
Earlier work this paper cites.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules, 2020b
Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann · 2011
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole von Lilienfeld · 2014
Earlier work this paper cites.
Quantum-chemical insights from deep tensor neural networks
Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R. Müller, and Alexandre Tkatchenko · 2017
Earlier work this paper cites.
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J. S. Smith, O. Isayev, and A. E. Roitberg · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry, 2017
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Earlier work this paper cites.
Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt, and Klaus-Robert Müller · 2017
Earlier work this paper cites.
Graph neural networks: A review of methods and applications, 2018
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
Earlier work this paper cites.
Relational inductive biases, deep learning, and graph networks, 2018
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
Earlier work this paper cites.
3d steerable cnns: Learning rotationally equivariant features in volumetric data, 2018
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
Earlier work this paper cites.
SchNet – a deep learning architecture for molecules and materials
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller · 2018
Cited alongside, same era.
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Cited alongside, same era.
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, and Alexandre Tkatchenko · 2018
Cited alongside, same era.
Applications of machine learning in drug discovery and development
Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, and Shanrong Zhao · 2019
Cited alongside, same era.
Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong-Son Hy, and Risi Kondor · 2019
Cited alongside, same era.
Spherical message passing for 3d graph networks, 2021
Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
Later among the works it cites.
E(n) equivariant graph neural networks, 2021
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
Later among the works it cites.
Machine learning of solvent effects on molecular spectra and reactions
Michael Gastegger, Kristof T. Schütt, and Klaus-Robert Müller · 2021
Later among the works it cites.
Linear atomic cluster expansion force fields for organic molecules: Beyond RMSE
Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E. A. Allen, Daniel J. Cole, Christoph Ortner, and Gábor Csányi · 2021
Later among the works it cites.
Torchmd-net: Equivariant transformers for neural network based molecular potentials, 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T. Unke and Markus Meuwly · 2019
Cited alongside, same era.
Machine learning for molecular simulation
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, and Cecilia Clementi · 2020
Cited alongside, same era.
Molecular dynamics with neural network potentials
Michael Gastegger and Philipp Marquetand · 2020
Cited alongside, same era.
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules
Justin S. Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrian E. Roitberg, Olexandr Isayev, and Sergei Tretiak · 2020
Cited alongside, same era.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges, 2021
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
Cited alongside, same era.
Geometric and physical quantities improve e(3) equivariant message passing, 2021
Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, and Max Welling · 2021
Cited alongside, same era.
Equivariant message passing for the prediction of tensorial properties and molecular spectra, 2021
Kristof T. Schütt, Oliver T. Unke, and Michael Gastegger · 2021
Cited alongside, same era.
Philipp Thölke and Gianni De Fabritiis · 2022
Later among the works it cites.
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky · 2022
Later among the works it cites.
NewtonNet: a newtonian message passing network for deep learning of interatomic potentials and forces
Mojtaba Haghighatlari, Jie Li, Xingyi Guan, Oufan Zhang, Akshaya Das, Christopher J Stein, Farnaz Heidar-Zadeh, Meili Liu, Martin Head-Gordon, Luke Bertels, Hongxia Hao, Itai Leven, and Teresa Head-Gordon · 2022
Later among the works it cites.
Spatial attention kinetic networks with e(n)-equivariance, 2023
Yuanqing Wang and John D. Chodera · 2023
Closest in time.
Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
Dávid Péter Kovács, Ilyes Batatia, Eszter Sára Arany, and Gábor Csányi · 2023
Closest in time.
SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials
Peter Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni De Fabritiis, and Thomas E. Markland · 2023
Closest in time.
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, and Michael Gastegger · 2023
Closest in time.
Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M Smedskjaer, Sayan Ranu, and N M Anoop Krishnan · 2023
Closest in time.