The Design Space of E(3)-Equivariant Atom-Centered Interatomic
Potentials

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

13 May 2022

Simon L. Batzner

Albert Musaelian

Christoph Ortner

Papers citing "The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials"

16 / 16 papers shown

Title
Representing spherical tensors with scalar-based machine-learning models Michelangelo Domina Filippo Bigi Paolo Pegolo Michele Ceriotti 45 0 0 08 May 2025
Towards Faster and More Compact Foundation Models for Molecular Property Prediction Yasir Ghunaim Andrés Villa Gergo Ignacz Gyorgy Szekely Motasem Alfarra Bernard Ghanem AI4CE 84 0 0 28 Apr 2025
Optimal Invariant Bases for Atomistic Machine Learning Alice Allen Emily Shinkle Roxana Bujack Nicholas Lubbers 37 0 0 30 Mar 2025
A practical guide to machine learning interatomic potentials -- Status and future Ryan Jacobs D. Morgan Siamak Attarian Jun Meng Chen Shen ... K. J. Schmidt So Takamoto Aidan Thompson Julia Westermayr Brandon M. Wood 55 4 0 12 Mar 2025
Discovery of sustainable energy materials via the machine-learned material space Malte Grunert Max Großmann Erich Runge 31 0 0 10 Jan 2025
Predicting ionic conductivity in solids from the machine-learned potential energy landscape Artem Maevskiy Alexandra Carvalho Emil Sataev Volha Turchyna Keian Noori Aleksandr Rodin A. H. Castro Neto Andrey E. Ustyuzhanin 37 0 0 11 Nov 2024
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models Luis Barroso-Luque Muhammed Shuaibi Xiang Fu Brandon M. Wood Misko Dzamba Meng Gao Ammar Rizvi C. L. Zitnick Zachary W. Ulissi AI4CE PINN 32 16 0 16 Oct 2024
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds Ilyes Batatia 18 0 0 16 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
Beyond MD17: the reactive xxMD dataset Zihan Pengmei Junyu Liu Yinan Shu 21 6 0 22 Aug 2023
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size Albert Musaelian A. Johansson Simon L. Batzner Boris Kozinsky 27 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 27 33 0 07 Apr 2023
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism Zimu Li Zihan Pengmei Han Zheng Erik H. Thiede Junyu Liu Risi Kondor 27 2 0 14 Nov 2022
Hierarchical Learning in Euclidean Neural Networks Joshua A. Rackers P. Rao 28 1 0 10 Oct 2022
BIP: Boost Invariant Polynomials for Efficient Jet Tagging José M. Muñoz Ilyes Batatia Christoph Ortner 24 14 0 17 Jul 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 174 1,104 0 27 Apr 2021