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Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures.
Rationale for mixing exact exchange with density functional approximations
John P. Perdew, Matthias Ernzerhof, and Kieron Burke · 1996
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Toward reliable density functional methods without adjustable parameters: The PBE0 model
Carlo Adamo and Vincenzo Barone · 1999
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Canonical sampling through velocity rescaling
Giovanni Bussi, Davide Donadio, and Michele Parrinello · 2007
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Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods
Dominik Marx and Jürg Hutter · 2009
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Geometry optimization
H. Bernhard Schlegel · 2011
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Fast and accurate modeling of molecular atomization energies with machine learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O Anatole Von Lilienfeld · 2012
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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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
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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IARPA / PNNL Liquid Phase IR Spectra
Tanya L. Myers, Russell G. Tonkyn, Ashley M. Oeck, Tyler O. Danby, John S. Loring, Matthew S. Taubman, Stephen W. Sharpe, Jerome C. Birnbaum, and Timothy J. Johnson · 2017
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IARPA / PNNL Liquid Phase IR Spectra
Tanya L. Myers, Russell G. Tonkyn, Ashley M. Oeck, Tyler O. Danby, John S. Loring, Matthew S. Taubman, Stephen W. Sharpe, Jerome C. Birnbaum, and Timothy J. Johnson · 2017
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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
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess E. Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela, Huziel E Sauceda, Klaus-Robert Müller, and Alexandre Tkatchenko · 2018
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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, and Weinan E · 2018
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Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela, Huziel E Sauceda, Klaus-Robert Müller, and Alexandre Tkatchenko · 2018
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Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins
Hao Geng, Fangfang Chen, Jing Ye, and Fan Jiang · 2019
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Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T Unke and Markus Meuwly · 2019
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A generally applicable atomic-charge dependent london dispersion correction
Eike Caldeweyher, Sebastian Ehlert, Andreas Hansen, Hagen Neugebauer, Sebastian Spicher, Christoph Bannwarth, and Stefan Grimme · 2019
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Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
Jonathan P. Mailoa, Mordechai Kornbluth, Simon L. Batzner, Georgy Samsonidze, Stephen T. Lam, Chris Ablitt, Nicola Molinari, and Boris Kozinsky · 2019
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Machine learning for molecular simulation
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, and Cecilia Clementi · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Density functional model for van der Waals interactions: Unifying many-body atomic approaches with nonlocal functionals
Jan Hermann and Alexandre Tkatchenko · 2020
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules, 2020
Johannes Gasteiger, Shankari Giri, Johannes T. Margraf, and Stephan Günnemann · 2020
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Molecular Dynamics Simulation of the Interaction of Two Linear Battacin Analogs with Model Gram-Positive and Gram-Negative Bacterial Cell Membranes
Aparajita Chakraborty, Elisey Kobzev, Jonathan Chan, Gayan Heruka de Zoysa, Vijayalekshmi Sarojini, Thomas J. Piggot, and Jane R Allison · 2021
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Highly accurate protein structure prediction with AlphaFold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
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Combining machine learning and computational chemistry for predictive insights into chemical systems
John A Keith, Valentin Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus-Robert Muller, and Alexandre Tkatchenko · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof Schütt, Oliver Unke, and Michael Gastegger · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velickovic · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Forcenet: A graph neural network for large-scale quantum calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, and C. Lawrence Zitnick · 2021
Cited alongside, same era.
Gemnet: Universal directional graph neural networks for molecules
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
Cited alongside, same era.
Systematic generalization with edge transformers
Leon Bergen, Timothy J. O’Donnell, and Dzmitry Bahdanau · 2021
Scaling rectified flow transformers for high-resolution image synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al · 2024
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Emergent equivariance in deep ensembles
Jan E. Gerken and Pan Kessel · 2024
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Ensembles provably learn equivariance through data augmentation
Oskar Nordenfors and Axel Flinth · 2024
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The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Filippo Bigi, Marcel F. Langer, and Michele Ceriotti · 2024
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Relaxed equivariance via multitask learning
Ahmed A. A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni, and Michael M. Bronstein · 2024
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A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, and Jörg Behler · 2021
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Spherical message passing for 3d graph networks
Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, and Jörg Behler · 2021
Cited alongside, same era.
Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects
Oliver T Unke, Stefan Chmiela, Michael Gastegger, Kristof T Schütt, Huziel E Sauceda, and Klaus-Robert Müller · 2021
Cited alongside, same era.
Vibrational spectroscopy by means of first-principles molecular dynamics simulations
Edward Ditler and Sandra Luber · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Later among the works it cites.
Does equivariance matter at scale?
Johann Brehmer, Sönke Behrends, Pim de Haan, and Taco Cohen · 2024
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Orb: A fast, scalable neural network potential
Mark Neumann, James Gin, Benjamin Rhodes, Steven Bennett, Zhiyi Li, Hitarth Choubisa, Arthur Hussey, and Jonathan Godwin · 2024
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Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations
Yi-Lun Liao, Brandon M. Wood, Abhishek Das, and Tess E. Smidt · 2024
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Smooth, exact rotational symmetrization for deep learning on point clouds, 2024
Sergey N. Pozdnyakov and Michele Ceriotti · 2024
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Towards principled graph transformers
Luis Müller, Daniel Kusuma, Blai Bonet, and Christopher Morris · 2024
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Probing the effects of broken symmetries in machine learning
Marcel F. Langer, Sergey N. Pozdnyakov, and Michele Ceriotti · 2024
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The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains, 2024
Eric Qu and Aditi S. Krishnapriyan · 2024
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Weisfeiler leman for euclidean equivariant machine learning
Snir Hordan, Tal Amir, and Nadav Dym · 2024
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The dark side of the forces: assessing non-conservative force models for atomistic machine learning
Filippo Bigi, Marcel F. Langer, and Michele Ceriotti · 2024
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Orb-v3: atomistic simulation at scale
Benjamin Rhodes, Sander Vandenhaute, Vaidotas Šimkus, James Gin, Jonathan Godwin, Tim Duignan, and Mark Neumann · 2025
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High-performance training and inference for deep equivariant interatomic potentials
Chuin Wei Tan, Marc L Descoteaux, Mit Kotak, Gabriel de Miranda Nascimento, Seán R Kavanagh, Laura Zichi, Menghang Wang, Aadit Saluja, Yizhong R Hu, Tess Smidt, et al · 2025
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The qcml dataset – quantum chemistry reference data from 33.5m dft and 14.7b semi-empirical calculations
Stefan Ganscha, Oliver T. Unke, Daniel Ahlin, Hartmut Maennel, Sergii Kashubin, and Klaus-Robert Müller · 2025
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Universal interatomic potentials with dft for understanding orbital localization in polydimethylsiloxane-amorphous silica nanocomposites
Carson Farmer and Hector Medina · 2025
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Reconstructions and dynamics of b e t a \\ beta -lithium thiophosphate surfaces
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Transformers discover molecular structure without graph priors, 2025
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Uma: A family of universal models for atoms
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Learning smooth and expressive interatomic potentials for physical property prediction
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URL https://webbook.nist.gov/chemistry/
NIST Chemistry WebBook, NIST Standard Reference Database Number 69, 2025 · 2025
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URL https://webbook.nist.gov/chemistry/
NIST Chemistry WebBook, NIST Standard Reference Database Number 69, 2025 · 2025
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Learning smooth and expressive interatomic potentials for physical property prediction
Xiang Fu, Brandon M. Wood, Luis Barroso-Luque, Daniel S. Levine, Meng Gao, Misko Dzamba, and C. Lawrence Zitnick · 2025
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Pushing the limits of unconstrained machine-learned interatomic potentials
Filippo Bigi, Paolo Pegolo, Arslan Mazitov, and Michele Ceriotti · 2026
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torch.compile with aotautograd does not support double backwards
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Learning local equivariant representations for large-scale atomistic dynamics
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, and Boris Kozinsky · 2041
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Beyond md17: the reactive xxmd dataset
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