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Understanding the interactions of atoms such as forces in 3D atomistic systems is fundamental to many applications like molecular dynamics and catalyst design.
Improved adsorption energetics within density-functional theory using revised perdew-burke-ernzerhof functionals
B. Hammer, L. B. Hansen, and J. K. Nørskov · 1999
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Extracting and composing robust features with denoising autoencoders
Pascal Vincent, H. Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Pascal Vincent, H. Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol · 2010
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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Lars Ruddigkeit, Ruud van Deursen, Lorenz C. Blum, and Jean-Louis Reymond · 2012
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen T. Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, and Kristin Aslaug Persson · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q. Weinberger · 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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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Pubchemqc project: A large-scale first-principles electronic structure database for data-driven chemistry
Maho Nakata and Tomomi Shimazaki · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
K. T. Schütt, P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, and K.-R. Müller · 2017
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Quantum-chemical insights from deep tensor neural networks
Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R. Müller, and Alexandre Tkatchenko · 2017
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Ani-1: A data set of 20m off-equilibrium dft calculations for organic molecules
Justin S. Smith, Olexandr Isayev, and Adrian E. Roitberg · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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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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Spherical CNNs
Taco S. Cohen, Mario Geiger, Jonas Köhler, and Max Welling · 2018
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Clebsch–gordan nets: a fully fourier space spherical convolutional neural network
Risi Kondor, Zhen Lin, and Shubhendu Trivedi · 2018
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Interactive supercomputing on 40,000 cores for machine learning and data analysis
Albert Reuther, Jeremy Kepner, Chansup Byun, Siddharth Samsi, William Arcand, David Bestor, Bill Bergeron, Vijay Gadepally, Michael Houle, Matthew Hubbell, Michael Jones, Anna Klein, Lauren Milechin, Julia Mullen, Andrew Prout, Antonio Rosa, Charles Yee, and Peter Michaleas · 2018
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Less is more: sampling chemical space with active learning
Justin S. Smith, Benjamin Tyler Nebgen, Nicholas Lubbers, Olexandr Isayev, and Adrian E. Roitberg · 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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3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, and Taco Cohen · 2018
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Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Se(3)-transformers: 3d roto-translation equivariant attention networks
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Energy-motivated equivariant pretraining for 3d molecular graphs
Rui Jiao, Jiaqi Han, Wenbing Huang, Yu Rong, and Yang Liu · 2022
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AdsorbML: Accelerating adsorption energy calculations with machine learning
Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C Lawrence Zitnick, and Zachary W Ulissi · 2022
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Equivariant graph attention networks for molecular property prediction
Tuan Le, Frank Noé, and Djork-Arné Clevert · 2022
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Learning local equivariant representations for large-scale atomistic dynamics
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Fabian Fuchs, Daniel E. Worrall, Volker Fischer, and Max Welling · 2020
Cited alongside, same era.
Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
Cited alongside, same era.
Relevance of rotationally equivariant convolutions for predicting molecular properties
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, and Frank Noé · 2020
Cited alongside, same era.
Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W. Battaglia · 2020
Cited alongside, same era.
Geometric prediction: Moving beyond scalars
Raphael J. L. Townshend, Brent Townshend, Stephan Eismann, and Ron O. Dror · 2020
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2021
Cited alongside, same era.
Albert Musaelian, Simon Batzner, Anders Johansson, Lixin Sun, Cameron J. Owen, Mordechai Kornbluth, and Boris Kozinsky · 2022
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Equivariant transformers for neural network based molecular potentials
Philipp Thölke and Gianni De Fabritiis · 2022
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The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysis
Richard Tran*, Janice Lan*, Muhammed Shuaibi*, Brandon Wood*, Siddharth Goyal*, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, Anuroop Sriram, Zachary Ulissi, and C. Lawrence Zitnick · 2022
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Spherical channels for modeling atomic interactions
Larry Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, and Brandon Wood · 2022
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Scaling vision transformers to 22 billion parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, and Neil Houlsby · 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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FAENet: Frame averaging equivariant GNN for materials modeling
Alexandre Agm Duval, Victor Schmidt, Alex Hernández-García, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, and David Rolnick · 2023
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Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations
Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi Jaakkola · 2023
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs
Yi-Lun Liao and Tess Smidt · 2023
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EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Yi-Lun Liao, Brandon Wood, Abhishek Das*, and Tess Smidt* · 2023
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Molecular geometry pretraining with SE(3)-invariant denoising distance matching
Shengchao Liu, Hongyu Guo, and Jian Tang · 2023
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Albert Musaelian, Anders Johansson, Simon Batzner, and Boris Kozinsky · 2023
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Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
Saro Passaro and C Lawrence Zitnick · 2023
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A recipe for cracking the quantum scaling limit with machine learned electron densities
Joshua A Rackers, Lucas Tecot, Mario Geiger, and Tess E Smidt · 2023
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UL2: Unifying language learning paradigms
Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Steven Zheng, Denny Zhou, Neil Houlsby, and Donald Metzler · 2023
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Denoise pretraining on nonequilibrium molecules for accurate and transferable neural potentials
Yuyang Wang, Changwen Xu, Zijie Li, and Amir Barati Farimani · 2023
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Pre-training via denoising for molecular property prediction
Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin · 2023
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A hitchhiker’s guide to geometric gnns for 3d atomic systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, and Michael Bronstein · 2024
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