Pushing the Limits of All-Atom Geometric Graph Neural Networks:
Pre-Training, Scaling and Zero-Shot Transfer

Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer

29 October 2024

Huzefa Rangwala

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Papers citing "Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling and Zero-Shot Transfer"

19 / 19 papers shown

Title
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching Shengchao Liu Hongyu Guo Jian Tang 97 80 0 27 Jun 2022
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields Ilyes Batatia D. P. Kovács G. Simm Christoph Ortner Gábor Csányi 91 501 0 15 Jun 2022
Pre-training via Denoising for Molecular Property Prediction Sheheryar Zaidi Michael Schaarschmidt James Martens Hyunjik Kim Yee Whye Teh Alvaro Sanchez-Gonzalez Peter W. Battaglia Razvan Pascanu Jonathan Godwin DiffM AI4CE 114 127 0 31 May 2022
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics Albert Musaelian Simon L. Batzner A. Johansson Lixin Sun Cameron J. Owen M. Kornbluth Boris Kozinsky 101 463 0 11 Apr 2022
Training Compute-Optimal Large Language Models Jordan Hoffmann Sebastian Borgeaud A. Mensch Elena Buchatskaya Trevor Cai ... Karen Simonyan Erich Elsen Jack W. Rae Oriol Vinyals Laurent Sifre AI4TS 211 1,987 0 29 Mar 2022
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials Philipp Thölke Gianni De Fabritiis AI4CE 101 197 0 05 Feb 2022
Learning Transferable Visual Models From Natural Language Supervision Alec Radford Jong Wook Kim Chris Hallacy Aditya A. Ramesh Gabriel Goh ... Amanda Askell Pamela Mishkin Jack Clark Gretchen Krueger Ilya Sutskever CLIP VLM 1.0K 29,926 0 26 Feb 2021
Equivariant message passing for the prediction of tensorial properties and molecular spectra Kristof T. Schütt Oliver T. Unke M. Gastegger 110 544 0 05 Feb 2021
Scaling Laws for Transfer Danny Hernandez Jared Kaplan T. Henighan Sam McCandlish 95 251 0 02 Feb 2021
Score-Based Generative Modeling through Stochastic Differential Equations Yang Song Jascha Narain Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon Ben Poole DiffM SyDa 385 6,592 0 26 Nov 2020
Denoising Diffusion Implicit Models Jiaming Song Chenlin Meng Stefano Ermon VLM DiffM 304 7,500 0 06 Oct 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta J. Leskovec 311 2,752 0 02 May 2020
Directional Message Passing for Molecular Graphs Johannes Klicpera Janek Groß Stephan Günnemann 143 881 0 06 Mar 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 651 4,925 0 23 Jan 2020
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova VLM SSL SSeg 1.8K 95,324 0 11 Oct 2018
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 164 361 0 30 Oct 2017
VAMPnets: Deep learning of molecular kinetics Andreas Mardt Luca Pasquali Hao Wu Frank Noé 70 546 0 16 Oct 2017
DeepSF: deep convolutional neural network for mapping protein sequences to folds Jie Hou B. Adhikari Jianlin Cheng 70 202 0 04 Jun 2017
Deep Unsupervised Learning using Nonequilibrium Thermodynamics Jascha Narain Sohl-Dickstein Eric A. Weiss Niru Maheswaranathan Surya Ganguli SyDa DiffM 312 7,035 0 12 Mar 2015