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Machine learning force fields (MLFFs) are an attractive alternative to ab-initio methods for molecular dynamics (MD) simulations.
High-temperature equation of state by a perturbation method. i. nonpolar gases
Robert W. Zwanzig · 1954
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Unified approach for molecular dynamics and density-functional theory
R. Car and M. Parrinello · 1985
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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A second generation force field for the simulation of proteins, nucleic acids, and organic molecules
Wendy D. Cornell, Piotr Cieplak, Christopher I. Bayly, Ian R. Gould, Kenneth M. Merz, David M. Ferguson, David C. Spellmeyer, Thomas Fox, James W. Caldwell, and Peter A. Kollman · 1995
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Generalized gradient approximation made simple
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Understanding Molecular Simulation: From Algorithms to Applications
Daan Frenkel and Berend Smit · 2001
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Ab initio quantum chemistry: Methodology and applications
Richard A. Friesner · 2005
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Flexible simple point-charge water model with improved liquid-state properties
Yujie Wu, Harald L. Tepper, and Gregory A. Voth · 2006
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Accurate sampling using langevin dynamics
Giovanni Bussi and Michele Parrinello · 2007
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The martini force field: coarse grained model for biomolecular simulations
S. J. Marrink, H. J. Risselada, S. Yefimov, D. P. Tieleman, and A. H. De Vries · 2007
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Accurate molecular van der waals interactions from ground-state electron density and free-atom reference data
Alexandre Tkatchenko and Matthias Scheffler · 2009
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Proton ordering in cubic ice and hexagonal ice; a potential new ice phase–xic
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A new multiscale algorithm and its application to coarse-grained peptide models for self-assembly
Scott P. Carmichael and M. Scott Shell · 2012
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Accurate and efficient method for many-body van der waals interactions
Alexandre Tkatchenko, Robert A. DiStasio, Roberto Car, and Matthias Scheffler · 2012
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Perspective: coarse-grained models for biomolecular systems
W. G. Noid · 2013
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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S. Gould, B. Fernando, A. Cherian, P. Anderson, R. S. Cruz, and E. Guo · 2016
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Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J. Zico Kolter · 2017
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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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Prediction errors of molecular machine learning models lower than hybrid dft error
FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, O Vinyals, S Kearnes, PF Riley, and OA von Lilienfeld · 2017
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The atomic simulation environment — a python library for working with atoms
Ask Hjorth Larsen, Jens Jørgen Mortensen, Jakob Blomqvist, Ivano E. Castelli, Rune Christensen, Marcin Dulak, Jesper Friis, Michael N. Groves, Bjørk Hammer, Cory Hargus, et al · 2017
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Ab initio thermodynamics of liquid and solid water
Bingqing Cheng, Edgar A. Engel, Jörg Behler, and Michele Ceriotti · 2018
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Data-driven learning of total and local energies in elemental boron
Volker L Deringer, Chris J Pickard, and Gábor Csányi · 2018
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Quantum-accurate spectral neighbor analysis potential models for ni-mo binary alloys and fcc metals
Xiang-Guo Li, Chongze Hu, Chi Chen, Zhi Deng, Jian Luo, and Shyue Ping Ong · 2018
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Nuclear quantum effects enter the mainstream
TE Markland and M Ceriotti · 2018
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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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Less is more: Sampling chemical space with active learning
Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, and Adrian E. Roitberg · 2018
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Neural ordinary differential equations, 2019
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2019
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Learning protein structure with a differentiable simulator
John Ingraham, Adam Riesselman, Chris Sander, and Debora Marks · 2019
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Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
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An entropy-maximization approach to automated training set generation for interatomic potentials
Mariia Karabin and Danny Perez · 2020
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Committee neural network potentials control generalization errors and enable active learning
Christoph Schran, Krystof Brezina, and Ondrej Marsalek · 2020
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On-the-fly active learning of interpretable bayesian force fields for atomistic rare events
J. Vandermause, S.B. Torrisi, S. Batzner, et al · 2020
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Differentiable molecular simulations for control and learning, 2020
Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements
S. Takamoto, C. Shinagawa, D. Motoki, et al · 2022
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A foundation model for atomistic materials chemistry, 2023
Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M. Elena, Dávid P. Kovács, Janosh Riebesell, Xavier R. Advincula, Mark Asta, William J. Baldwin, Noam Bernstein, Arghya Bhowmik, Samuel M. Blau, Vlad Cărare, James P. Darby, Sandip De, Flaviano Della Pia, Volker L. Deringer, Rokas Elijošius, Zakariya El-Machachi, Edvin Fako, Andrea C. Ferrari, Annalena Genreith-Schriever, Janine George, Rhys E. A. Goodall, Clare P. Grey, Shuang Han, Will Handley, Hendrik H. Heenen, Kersti Hermansson, Christian Holm, Jad Jaafar, Stephan Hofmann, Konstantin S. Jakob, Hyunwook Jung, Venkat Kapil, Aaron D. Kaplan, Nima Karimitari, Namu Kroupa, Jolla Kullgren, Matthew C. Kuner, Domantas Kuryla, Guoda Liepuoniute, Johannes T. Margraf, Ioan-Bogdan Magdău, Angelos Michaelides, J. Harry Moore, Aakash A. Naik, Samuel P. Niblett, Sam Walton Norwood, Niamh O’Neill, Christoph Ortner, Kristin A. Persson, Karsten Reuter, Andrew S. Rosen, Lars L. Schaaf, Christoph Schran, Eric Sivonxay, Tamás K. Stenczel, Viktor Svahn, Christopher Sutton, Cas van der Oord, Eszter Varga-Umbrich, Tejs Vegge, Martin Vondrák, Yangshuai Wang, William C. Witt, Fabian Zills, and Gábor Csányi · 2023
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Egraffbench: Evaluation of equivariant graph neural network force fields for atomistic simulations, 2023
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
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Wujie Wang, Simon Axelrod, and Rafael Gómez-Bombarelli · 2020
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Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning
Gregory Fonseca, Igor Poltavsky, Valentin Vassilev-Galindo, and Alexandre Tkatchenko · 2021
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In Conference on Neural Information Processing Systems (NeurIPS) , 2021
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
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Forcenet: A graph neural network for large-scale quantum calculations, 2021
Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, and C. Lawrence Zitnick · 2021
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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
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Diffusion monte carlo with fictitious masses finds holes in potential energy surfaces
J. Li, C. Qu, and J. M. Bowman · 2021
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Searching configurations in uncertainty space: Active learning of high-dimensional neural network reactive potentials
Qidong Lin, Liang Zhang, Yaolong Zhang, and Bin Jiang · 2021
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Optimizing molecular potential models by imposing kinetic constraints with path reweighting
Peter G. Bolhuis, Z. Faidon Brotzakis, and Bettina G. Keller · 2023
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Navigating protein landscapes with a machine-learned transferable coarse-grained model, 2023
Nicholas E. Charron, Felix Musil, Andrea Guljas, Yaoyi Chen, Klara Bonneau, Aldo S. Pasos-Trejo, Jacopo Venturin, Daria Gusew, Iryna Zaporozhets, Andreas Krämer, Clark Templeton, Atharva Kelkar, Aleksander E. P. Durumeric, Simon Olsson, Adrià Pérez, Maciej Majewski, Brooke E. Husic, Ankit Patel, Gianni De Fabritiis, Frank Noé, and Cecilia Clementi · 2023
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Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling
Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J Bartel, and Gerbrand Ceder · 2023
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Efficient interatomic descriptors for accurate machine learning force fields of extended molecules
A. Kabylda, V. Vassilev-Galindo, S. Chmiela, I. Poltavsky, and A. Tkatchenko · 2023
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Rescuing off-equilibrium simulation data through dynamic experimental data with dynammo
Christopher Kolloff and Simon Olsson · 2023
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Mace-off23: Transferable machine learning force fields for organic molecules, 2023
Dávid Péter Kovács, J. Harry Moore, Nicholas J. Browning, Ilyes Batatia, Joshua T. Horton, Venkat Kapil, William C. Witt, Ioan-Bogdan Magdău, Daniel J. Cole, and Gábor Csányi · 2023
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Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
Boris Kozinsky, Albert Musaelian, Anders Johansson, and Simon Batzner · 2023
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Learning continuous models for continuous physics
Aditi S Krishnapriyan, Alejandro F Queiruga, N Benjamin Erichson, and Michael W Mahoney · 2023
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Uncertainty-driven dynamics for active learning of interatomic potentials
M. Kulichenko, K. Barros, N. Lubbers, et al · 2023
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Machine learning coarse-grained potentials of protein thermodynamics
M. Majewski, A. Pérez, P. Thölke, et al · 2023
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Scaling deep learning for materials discovery
A. Merchant, S. Batzner, S.S. Schoenholz, et al · 2023
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How to validate machine-learned interatomic potentials
Joe D. Morrow, John L. A. Gardner, and Volker L. Deringer · 2023
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Top-down machine learning of coarse-grained protein force fields
Carles Navarro, Maciej Majewski, and Gianni De Fabritiis · 2023
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Learning differentiable solvers for systems with hard constraints
Geoffrey Négiar, Michael W. Mahoney, and Aditi Krishnapriyan · 2023
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Accurate machine learning force fields via experimental and simulation data fusion, 2023
Sebastien Röcken and Julija Zavadlav · 2023
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From molecules to materials: Pre-training large generalizable models for atomic property prediction, 2023
Nima Shoghi, Adeesh Kolluru, John R. Kitchin, Zachary W. Ulissi, C. Lawrence Zitnick, and Brandon M. Wood · 2023
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Data efficiency and extrapolation trends in neural network interatomic potentials
Joshua A. Vita and Daniel Schwalbe-Koda · 2023
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Differentiable simulations for enhanced sampling of rare events, 2023
Martin Šípka, Johannes C. B. Dietschreit, Lukáš Grajciar, and Rafael Gómez-Bombarelli · 2023
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Artificial intelligence driving materials discovery? perspective on the article: Scaling deep learning for materials discovery
Anthony K Cheetham and Ram Seshadri · 2024
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Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
Fuchun Ge, Ran Wang, Chen Qu, Peikun Zheng, Apurba Nandi, Riccardo Conte, Paul L. Houston, Joel M. Bowman, and Pavlo O. Dral · 2024
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Reversible molecular simulation for training classical and machine learning force fields
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Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling
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chemtrain: Learning deep potential models via automatic differentiation and statistical physics
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Refining potential energy surface through dynamical properties via differentiable molecular simulation
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