E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

8 January 2021

Papers citing "E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials"

50 / 396 papers shown

Title
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks D. M. Nguyen Nina Lukashina Tai Nguyen An T. Le TrungTin Nguyen Nhat Ho Jan Peters Daniel Sonntag Viktor Zaverkin Mathias Niepert 36 5 0 03 Feb 2024
LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields Joshua A. Vita Amit Samanta Fei Zhou Vincenzo Lordi 25 2 0 01 Feb 2024
Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials I. Grega Ilyes Batatia Gábor Csányi Sri Karlapati Vikram S. Deshpande 22 3 0 30 Jan 2024
ZnTrack -- Data as Code Fabian Zills M. Schäfer S. Tovey Johannes Kästner Christian Holm 32 1 0 19 Jan 2024
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products Shengjie Luo Tianlang Chen Aditi S. Krishnapriyan 34 19 0 18 Jan 2024
Diffusion-Driven Generative Framework for Molecular Conformation Prediction Bobin Yang Jie Deng Zhenghan Chen Ruoxue Wu DiffM 31 0 0 22 Dec 2023
Molecular Hypergraph Neural Networks Junwu Chen Philippe Schwaller GNN 47 10 0 20 Dec 2023
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials Kyle Noordhoek Christopher J. Bartel AI4CE 27 6 0 18 Dec 2023
3DReact: Geometric deep learning for chemical reactions Puck van Gerwen K. Briling Charlotte Bunne Vignesh Ram Somnath Rubén Laplaza Andreas Krause C. Corminboeuf 3DV 39 6 0 13 Dec 2023
Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials Teddy Koker Keegan Quigley Eric Taw Kevin Tibbetts Lin Li 20 12 0 08 Dec 2023
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks Haoyu Li Shichang Zhang Longwen Tang Mathieu Bauchy Yizhou Sun AI4CE 48 1 0 08 Dec 2023
$Learning Polynomial Problems with $SL(2,\mathbb{R})$ Equivariance$ Learning Polynomial Problems with $SL(2,\mathbb{R})$ Equivariance Hannah Lawrence Mitchell Tong Harris 30 1 0 04 Dec 2023
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials Viktor Zaverkin David Holzmüller Henrik Christiansen Federico Errica Francesco Alesiani Makoto Takamoto Mathias Niepert Johannes Kastner AI4CE 32 13 0 03 Dec 2023
SAIBench: A Structural Interpretation of AI for Science Through Benchmarks Yatao Li Jianfeng Zhan 13 0 0 29 Nov 2023
Swallowing the Bitter Pill: Simplified Scalable Conformer Generation Yuyang Wang Ahmed A. A. Elhag Navdeep Jaitly J. Susskind Miguel Angel Bautista DiffM 27 20 0 27 Nov 2023
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation Ameya Daigavane Song Kim Mario Geiger Tess E. Smidt 28 8 0 27 Nov 2023
Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks Maria Bånkestad Keven M. Dorst Göran Widmalm Jerk Rönnols 40 2 0 21 Nov 2023
Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN Guang Yang Yuan Liu Lei Yang Bingyang Cao AI4CE 36 6 0 20 Nov 2023
Multiscale Hodge Scattering Networks for Data Analysis Naoki Saito Stefan C. Schonsheck Eugene Shvarts 40 1 0 17 Nov 2023
H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing Gian Marco Visani William Galvin Michael N. Pun Armita Nourmohammad 27 6 0 15 Nov 2023
Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks S. Thais D. Murnane AI4CE 31 4 0 06 Nov 2023
Gradual Optimization Learning for Conformational Energy Minimization Artem Tsypin L. Ugadiarov Kuzma Khrabrov Alexander Telepov Egor Rumiantsev Alexey Skrynnik Aleksandr I. Panov Dmitry Vetrov E. Tutubalina Artur Kadurin 24 1 0 05 Nov 2023
Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations D. Rothchild Andrew S. Rosen Eric Taw Connie Robinson Joseph E. Gonzalez Aditi S. Krishnapriyan DiffM 29 2 0 02 Nov 2023
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture Anuroop Sriram Sihoon Choi Xiaohan Yu Logan M. Brabson Abhishek Das Zachary W. Ulissi Matthew Uyttendaele A. Medford D. Sholl AI4CE 31 35 0 01 Nov 2023
The Role of Reference Points in Machine-Learned Atomistic Simulation Models Xiangyun Lei Weike Ye Joseph Montoya Tim Mueller Linda Hung Jens Hummelshoej 16 0 0 28 Oct 2023
Navigating protein landscapes with a machine-learned transferable coarse-grained model N. Charron Felix Musil Andrea Guljas Yaoyi Chen Klara Bonneau ... B. Husic Ankit Patel Gianni De Fabritiis Frank Noé C. Clementi AI4CE 16 13 0 27 Oct 2023
Learning Interatomic Potentials at Multiple Scales Xiang Fu Albert Musaelian Anders Johansson Tommi Jaakkola Boris Kozinsky 29 2 0 20 Oct 2023
Equivariant Transformer is all you need Akio Tomiya Yuki Nagai AI4CE 14 6 0 20 Oct 2023
Almost Equivariance via Lie Algebra Convolutions Daniel McNeela 29 6 0 19 Oct 2023
Scalable Diffusion for Materials Generation Mengjiao Yang KwangHwan Cho Amil Merchant Pieter Abbeel Dale Schuurmans Igor Mordatch E. D. Cubuk 29 39 0 18 Oct 2023
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design Xiang Fu Tian Xie Andrew S. Rosen Tommi Jaakkola Jake A. Smith DiffM 42 9 0 16 Oct 2023
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds Ilyes Batatia 26 0 0 16 Oct 2023
Equivariant Matrix Function Neural Networks Ilyes Batatia Lars L. Schaaf Huajie Chen Gábor Csányi Christoph Ortner Felix A. Faber 32 5 0 16 Oct 2023
On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space Zihan Zhou Ruiying Liu Tianshu Yu DiffM 30 0 0 07 Oct 2023
Lie Neurons: Adjoint-Equivariant Neural Networks for Semisimple Lie Algebras Tzu-Yuan Lin Minghan Zhu Maani Ghaffari 48 2 0 06 Oct 2023
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials Peter K. Eastman Raimondas Galvelis Raúl P. Peláez C. Abreu Stephen E. Farr ... Yuanqing Wang Ivy Zhang J. Chodera Gianni De Fabritiis T. Markland AI4CE VLM 38 37 0 04 Oct 2023
Fast, Expressive SE $(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space Erik J. Bekkers Sharvaree P. Vadgama Rob D. Hesselink P. A. V. D. Linden David W. Romero 21 24 0 04 Oct 2023
Spline-based neural network interatomic potentials: blending classical and machine learning models Joshua A Vita D. Trinkle 14 2 0 04 Oct 2023
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations Vaibhav Bihani Utkarsh Pratiush Sajid Mannan Tao Du Zhimin Chen Santiago Miret Matthieu Micoulaut M. Smedskjaer Sayan Ranu N. M. A. Krishnan 24 19 0 03 Oct 2023
Approximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images Su Yeon Chang Michele Grossi B. L. Saux S. Vallecorsa 51 13 0 03 Oct 2023
Backdiff: a diffusion model for generalized transferable protein backmapping Yikai Liu Ming Chen Guang Lin DiffM 27 2 0 03 Oct 2023
Algebras of actions in an agent's representations of the world Alexander Dean Eduardo Alonso Esther Mondragón 33 0 0 02 Oct 2023
Predicting emergence of crystals from amorphous matter with deep learning Muratahan Aykol Amil Merchant Simon L. Batzner Jennifer N. Wei E. D. Cubuk AI4CE 15 2 0 02 Oct 2023
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation Tuan Le Julian Cremer Frank Noé Djork-Arné Clevert Kristof T. Schütt DiffM 29 25 0 29 Sep 2023
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning He Zhang Siyuan Liu Jiacheng You Chang-Shu Liu Shuxin Zheng Ziheng Lu Tong Wang Nanning Zheng Bin Shao 14 18 0 28 Sep 2023
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite Stefania Costantini Gianluca Galletti Fabian Fritz Stefan Adami Nikolaus A. Adams 40 13 0 28 Sep 2023
Learning dislocation dynamics mobility laws from large-scale MD simulations N. Bertin Vasily V. Bulatov Fei Zhou AI4CE 22 8 0 25 Sep 2023
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields J. Frank Oliver T. Unke Klaus-Robert Muller Stefan Chmiela 26 3 0 21 Sep 2023
Band-gap regression with architecture-optimized message-passing neural networks Tim Bechtel Daniel T. Speckhard Jonathan Godwin Claudia Ambrosch-Draxl 24 0 0 12 Sep 2023
Molecular Conformation Generation via Shifting Scores Zihan Zhou Ruiying Liu Chaolong Ying Ruimao Zhang Tianshu Yu DiffM 29 2 0 12 Sep 2023