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Graph Neural Networks (GNNs) are the dominant architecture for molecular machine learning, particularly for molecular property prediction and machine learning interatomic potentials (MLIPs).
Language models are few-shot learners, 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2005
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Chemberta: Large-scale self-supervised pretraining for molecular property prediction, 2020
Seyone Chithrananda, Gabriel Grand, and Bharath Ramsundar · 2010
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
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 · 2010
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Scaling laws for autoregressive generative modeling, 2020
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, and Sam McCandlish · 2010
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Powergraph: distributed graph-parallel computation on natural graphs
Joseph E. Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, and Carlos Guestrin · 2012
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An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for tio2
Nongnuch Artrith and Alexander Urban · 2015
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In-datacenter performance analysis of a tensor processing unit, 2017
Norman P. Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, Rick Boyle, Pierre luc Cantin, Clifford Chao, Chris Clark, Jeremy Coriell, Mike Daley, Matt Dau, Jeffrey Dean, Ben Gelb, Tara Vazir Ghaemmaghami, Rajendra Gottipati, William Gulland, Robert Hagmann, C. Richard Ho, Doug Hogberg, John Hu, Robert Hundt, Dan Hurt, Julian Ibarz, Aaron Jaffey, Alek Jaworski, Alexander Kaplan, Harshit Khaitan, Andy Koch, Naveen Kumar, Steve Lacy, James Laudon, James Law, Diemthu Le, Chris Leary, Zhuyuan Liu, Kyle Lucke, Alan Lundin, Gordon MacKean, Adriana Maggiore, Maire Mahony, Kieran Miller, Rahul Nagarajan, Ravi Narayanaswami, Ray Ni, Kathy Nix, Thomas Norrie, Mark Omernick, Narayana Penukonda, Andy Phelps, Jonathan Ross, Matt Ross, Amir Salek, Emad Samadiani, Chris Severn, Gregory Sizikov, Matthew Snelham, Jed Souter, Dan Steinberg, Andy Swing, Mercedes Tan, Gregory Thorson, Bo Tian, Horia Toma, Erick Tuttle, Vijay Vasudevan, Richard Walter, Walter Wang, Eric Wilcox, and Doe Hyun Yoon · 2017
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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, 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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URL http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Richard Sutton, 2019 · 2019
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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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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi · 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, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi · 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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Machine learning force fields
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, and Klaus-Robert Müller · 2021
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MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Ilyes Batatia, David P Kovacs, Gregor Simm, Christoph Ortner, and Gábor Csányi · 2022
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Flashattention: Fast and memory-efficient exact attention with io-awareness, 2022
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré · 2022
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Training compute-optimal large language models, 2022
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre · 2022
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Pure transformers are powerful graph learners, 2022
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong · 2022
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Frame averaging for invariant and equivariant network design, 2022
Omri Puny, Matan Atzmon, Heli Ben-Hamu, Ishan Misra, Aditya Grover, Edward J. Smith, and Yaron Lipman · 2022
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Transition1x – a dataset for building generalizable reactive machine learning potentials, 2022
Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Jonas Busk, and Ole Winther · 2022
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Towards training billion parameter graph neural networks for atomic simulations, 2022
Anuroop Sriram, Abhishek Das, Brandon M. Wood, Siddharth Goyal, and C. Lawrence Zitnick · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature, 2022
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein · 2022
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Two for one: Diffusion models and force fields for coarse-grained molecular dynamics, 2023
Marloes Arts, Victor Garcia Satorras, Chin-Wei Huang, Daniel Zuegner, Marco Federici, Cecilia Clementi, Frank Noé, Robert Pinsler, and Rianne van den Berg · 2023
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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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Bowen Deng · 2023
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Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, and David Rolnick · 2023
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Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini · 2023
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Nathan C. Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gómez-Bombarelli, Connor W. Coley, and Vijay Gadepally · 2023
Md Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian · 2024
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Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, and Chelsea Finn · 2024
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Yi-Lun Liao, Brandon Wood, Abhishek Das, and Tess Smidt · 2024
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Uncertainty estimation and quantification for llms: A simple supervised approach, 2024
Linyu Liu, Yu Pan, Xiaocheng Li, and Guanting Chen · 2024
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Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi S. Jaakkola · 2023
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Applying large graph neural networks to predict transition metal complex energies using the tmqm_wb97mv data set, 2023
Aaron G Garrison, Javier Heras-Domingo, John R Kitchin, Gabriel dos Passos Gomes, Zachary W Ulissi, and Samuel M Blau · 2023
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On over-squashing in message passing neural networks: The impact of width, depth, and topology, 2023
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Mace-off23: Transferable machine learning force fields for organic molecules
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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Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar · 2023
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Efficient memory management for large language model serving with pagedattention, 2023
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Recipe for a general, powerful, scalable graph transformer, 2023
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2023
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Orb: A fast, scalable neural network potential, 2024
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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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Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Octo: An open-source generalist robot policy, 2024
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, and Sergey Levine · 2024
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Swallowing the bitter pill: Simplified scalable conformer generation, 2024
Yuyang Wang, Ahmed A. Elhag, Navdeep Jaitly, Joshua M. Susskind, and Miguel Angel Bautista · 2024
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