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Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost.
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Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 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 Dułak, Jesper Friis, Michael N Groves, Bjørk Hammer, Cory Hargus, Eric D Hermes, Paul C Jennings, Peter Bjerre Jensen, James Kermode, John R Kitchin, Esben Leonhard Kolsbjerg, Joseph Kubal, Kristen Kaasbjerg, Steen Lysgaard, Jón Bergmann Maronsson, Tristan Maxson, Thomas Olsen, Lars Pastewka, Andrew Peterson, Carsten Rostgaard, Jakob Schiøtz, Ole Schütt, Mikkel Strange, Kristian S Thygesen, Tejs Vegge, Lasse Vilhelmsen, Michael Walter, Zhenhua Zeng, and Karsten W Jacobsen · 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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Ani-1: an extensible neural network potential with dft accuracy at force field computational cost
Justin S Smith, Olexandr Isayev, and Adrian E Roitberg · 2017
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information
Oliver T Unke and Markus Meuwly · 2018
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco S Cohen · 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, et al · 2018
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
Roman Zubatyuk, Justin S Smith, Jerzy Leszczynski, and Olexandr Isayev · 2019
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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
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Differentiable molecular simulations for control and learning
Wujie Wang, Simon Axelrod, and Rafael Gómez-Bombarelli · 2020
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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, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi · 2021
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Efficient calculation of carrier scattering rates from first principles
Alex M Ganose, Junsoo Park, Alireza Faghaninia, Rachel Woods-Robinson, Kristin A Persson, and Anubhav Jain · 2021
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Gemnet: Universal directional graph neural networks for molecules
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Forcenet: A graph neural network for large-scale quantum calculations
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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 EA Allen, Daniel J Cole, Christoph Ortner, and Gábor Csányi · 2021
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Spherical message passing for 3d molecular graphs
Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji · 2021
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Open materials 2024 (omat24) inorganic materials dataset and models
Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C Lawrence Zitnick, and Zachary W Ulissi · 2024
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Graph atomic cluster expansion for semilocal interactions beyond equivariant message passing
Anton Bochkarev, Yury Lysogorskiy, and Ralf Drautz · 2024
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Does equivariance matter at scale?
Johann Brehmer, Sönke Behrends, Pim de Haan, and Taco Cohen · 2024
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Cartesian atomic cluster expansion for machine learning interatomic potentials
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
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Equivariant transformers for neural network based molecular potentials
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Review of computational approaches to predict the thermodynamic stability of inorganic solids
Christopher J Bartel · 2022
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MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
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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
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A universal graph deep learning interatomic potential for the periodic table
Chi Chen and Shyue Ping Ong · 2022
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Nutmeg and spice: models and data for biomolecular machine learning
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Phonon predictions with e (3)-equivariant graph neural networks
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Differentiable simulation to develop molecular dynamics force fields for disordered proteins
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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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Generalizing denoising to non-equilibrium structures improves equivariant force fields
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Universal machine learning interatomic potentials are ready for phonons
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Computing hydration free energies of small molecules with first principles accuracy
J Harry Moore, Daniel J Cole, and Gabor Csanyi · 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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Complexity of many-body interactions in transition metals via machine-learned force fields from the tm23 data set
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Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations
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Thermal conductivity predictions with foundation atomistic models
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The importance of being scalable: Improving the speed and accuracy of neural network interatomic potentials across chemical domains
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