MACE: Higher Order Equivariant Message Passing Neural Networks for Fast
and Accurate Force Fields

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

15 June 2022

Christoph Ortner

Papers citing "MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields"

16 / 66 papers shown

Title
SO(2)-Equivariant Downwash Models for Close Proximity Flight Henry Smith Ajay Shankar Jennifer Gielis J. Blumenkamp A. Prorok 29 7 0 30 May 2023
FAENet: Frame Averaging Equivariant GNN for Materials Modeling Alexandre Duval Victor Schmidt A. Garcia Santiago Miret Fragkiskos D. Malliaros Yoshua Bengio David Rolnick 34 55 0 28 Apr 2023
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size Albert Musaelian A. Johansson Simon L. Batzner Boris Kozinsky 32 48 0 20 Apr 2023
A new perspective on building efficient and expressive 3D equivariant graph neural networks Weitao Du Yuanqi Du Limei Wang Dieqiao Feng Guifeng Wang Shuiwang Ji Carla P. Gomes Zhixin Ma AI4CE 32 33 0 07 Apr 2023
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs Saro Passaro C. L. Zitnick 3DPC 34 79 0 07 Feb 2023
Complete Neural Networks for Complete Euclidean Graphs Snir Hordan Tal Amir S. Gortler Nadav Dym 3DPC 29 5 0 31 Jan 2023
Rigid Body Flows for Sampling Molecular Crystal Structures Jonas Köhler Michele Invernizzi P. D. Haan Frank Noé AI4CE 39 27 0 26 Jan 2023
Capturing long-range interaction with reciprocal space neural network Hongyu Yu Liangliang Hong Shiyou Chen X. Gong Hongjun Xiang 29 11 0 30 Nov 2022
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism Zimu Li Zihan Pengmei Han Zheng Erik H. Thiede Junyu Liu Risi Kondor 29 2 0 14 Nov 2022
Structure-based drug design with geometric deep learning Clemens Isert Kenneth Atz G. Schneider 53 104 0 19 Oct 2022
Hierarchical Learning in Euclidean Neural Networks Joshua A. Rackers P. Rao 30 1 0 10 Oct 2022
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials Cas van der Oord Matthias Sachs D. P. Kovács Christoph Ortner Gábor Csányi 41 64 0 09 Oct 2022
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials Peter K. Eastman P. Behara David L. Dotson Raimondas Galvelis John E. Herr ... J. Chodera Benjamin P. Pritchard Yuanqing Wang Gianni De Fabritiis T. Markland 34 105 0 21 Sep 2022
A smooth basis for atomistic machine learning Filippo Bigi Kevin K. Huguenin-Dumittan Michele Ceriotti D. Manolopoulos 22 6 0 05 Sep 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 174 1,106 0 27 Apr 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 206 1,240 0 08 Jan 2021