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The foundation model (FM) paradigm is transforming Machine Learning Force Fields (MLFFs), leveraging general-purpose representations and scalable training to perform a variety of computational chemistry tasks.
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
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Theoretical surface science and catalysis—calculations and concepts
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Structural relaxation made simple
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Toward computational materials design: the impact of density functional theory on materials research
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Fitnets: Hints for thin deep nets
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On large-batch training for deep learning: Generalization gap and sharp minima
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 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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Sobolev training for neural networks
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Bert: Pre-training of deep bidirectional transformers for language understanding
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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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Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on the fly with bayesian inference
Ryosuke Jinnouchi, Jonathan Lahnsteiner, Ferenc Karsai, Georg Kresse, and Menno Bokdam · 2019
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When does label smoothing help?
Rafael Müller, Simon Kornblith, and Geoffrey E Hinton · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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The materials project: Accelerating materials design through theory-driven data and tools
Anubhav Jain, Joseph Montoya, Shyam Dwaraknath, Nils ER Zimmermann, John Dagdelen, Matthew Horton, Patrick Huck, Donny Winston, Shreyas Cholia, Shyue Ping Ong, et al · 2020
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Learning local equivariant representations for large-scale atomistic dynamics, 2022
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, and Boris Kozinsky · 2022
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How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?
Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, and Johannes T Margraf · 2022
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Lammps-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
Aidan P Thompson, H Metin Aktulga, Richard Berger, Dan S Bolintineanu, W Michael Brown, Paul S Crozier, Pieter J In’t Veld, Axel Kohlmeyer, Stan G Moore, Trung Dac Nguyen, et al · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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A foundation model for atomistic materials chemistry, 2023
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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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Understanding knowledge distillation in non-autoregressive machine translation
Yiren Tang, Liangyou Lu, and Graham Neubig · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Open catalyst 2020 (oc20) dataset and community challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al · 2021
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Gemnet: Universal directional graph neural networks for molecules
Johannes Gasteiger, Florian Becker, and Stephan Günnemann · 2021
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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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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Accurate global machine learning force fields for molecules with hundreds of atoms
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T Unke, Adil Kabylda, Huziel E Sauceda, Alexandre Tkatchenko, and Klaus-Robert Müller · 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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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, et al · 2023
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Accelerating molecular graph neural networks via knowledge distillation
Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev, and Johannes Gasteiger · 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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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 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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The dark side of the forces: assessing non-conservative force models for atomistic machine learning, 2024
Filippo Bigi, Marcel Langer, and Michele Ceriotti · 2024
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Enabling efficient equivariant operations in the fourier basis via gaunt tensor products
Shengjie Luo, Tianlang Chen, and Aditi S Krishnapriyan · 2024
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Orb: A fast, scalable neural network potential, 2024
Mark Neumann, James Gin, Benjamin Rhodes, Steven Bennett, Zhiyi Li, Hitarth Choubisa, Arthur Hussey, and Jonathan Godwin · 2024
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Smooth, exact rotational symmetrization for deep learning on point clouds
Sergey Pozdnyakov and Michele Ceriotti · 2024
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Transferring knowledge from large foundation models to small downstream models, 2024
Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Yuyang Wang, and Andrew Gordon Wilson · 2024
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Eric Qu and Aditi S Krishnapriyan · 2024
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Sanjeev Raja, Ishan Amin, Fabian Pedregosa, and Aditi S Krishnapriyan · 2024
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On the design space between molecular mechanics and machine learning force fields, 2024
Yuanqing Wang, Kenichiro Takaba, Michael S Chen, Marcus Wieder, Yuzhi Xu, John ZH Zhang, Kuang Yu, Xinyan Wang, Linfeng Zhang, Daniel J Cole, et al · 2024
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