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

15 June 2022

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

43 / 43 papers shown

Title
Representing spherical tensors with scalar-based machine-learning models Michelangelo Domina Filippo Bigi Paolo Pegolo Michele Ceriotti 28 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 78 0 0 28 Apr 2025
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J. Havens Benjamin Kurt Miller Bing Yan Carles Domingo-Enrich Anuroop Sriram ... Brandon Amos Brian Karrer Xiang Fu Guan-Horng Liu Ricky T. Q. Chen DiffM 41 0 0 16 Apr 2025
Quantifying Robustness: A Benchmarking Framework for Deep Learning Forecasting in Cyber-Physical Systems Alexander Windmann Henrik S. Steude Daniel Boschmann Oliver Niggemann OOD AI4TS 26 0 0 04 Apr 2025
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems Yunyang Li Zaishuo Xia Lin Huang Xinran Wei Han Yang ... Zun Wang Chang-Shu Liu Jia Zhang Bin Shao Mark B. Gerstein 65 0 0 26 Feb 2025
Learning local equivariant representations for quantum operators Zhanghao Zhouyin Zixi Gan MingKang Liu S. K. Pandey Linfeng Zhang Qiangqiang Gu 70 2 0 28 Jan 2025
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials Matthias Holzenkamp Dongyu Lyu Ulrich Kleinekathöfer Peter Zaspel 29 0 0 10 Jan 2025
The dark side of the forces: assessing non-conservative force models for atomistic machine learning Filippo Bigi Marcel F. Langer Michele Ceriotti AI4CE 72 6 0 16 Dec 2024
NeuralDEM -- Real-time Simulation of Industrial Particulate Flows Benedikt Alkin Tobias Kronlachner Samuele Papa Stefan Pirker Thomas Lichtenegger Johannes Brandstetter PINN AI4CE 29 1 1 14 Nov 2024
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 27 0 0 11 Nov 2024
Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential Kentaro Hino Yuki Kurashige 29 0 0 31 Oct 2024
Relaxed Equivariance via Multitask Learning Ahmed A. A. Elhag T. Konstantin Rusch Francesco Di Giovanni Michael Bronstein 34 2 0 23 Oct 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 11 16 0 16 Oct 2024
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction Junjie Xu Artem Moskalev Tommaso Mansi Mangal Prakash Rui Liao AI4CE 23 1 0 15 Oct 2024
Learning Equivariant Non-Local Electron Density Functionals Nicholas Gao Eike Eberhard Stephan Günnemann 20 1 0 10 Oct 2024
Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups Zakhar Shumaylov Peter Zaika James Rowbottom Ferdia Sherry Melanie Weber Carola-Bibiane Schönlieb 22 1 0 03 Oct 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto Viktor Zaverkin Mathias Niepert AI4CE 48 0 0 23 Jul 2024
PlayMolecule pKAce: Small Molecule Protonation through Equivariant Neural Networks Nikolai Schapin Maciej Majewski Mariona Torrens-Fontanals Gianni de Fabritiis 19 1 0 15 Jul 2024
On the Expressive Power of Sparse Geometric MPNNs Yonatan Sverdlov Nadav Dym 40 1 0 02 Jul 2024
Evaluating representation learning on the protein structure universe Arian R. Jamasb Alex Morehead Chaitanya K. Joshi Zuobai Zhang Kieran Didi ... Charles Harris Jian Tang Jianlin Cheng Pietro Lio Tom L. Blundell SSL 21 12 0 19 Jun 2024
Grounding Continuous Representations in Geometry: Equivariant Neural Fields David R. Wessels David M. Knigge Samuele Papa Riccardo Valperga Sharvaree P. Vadgama E. Gavves Erik J. Bekkers 31 7 0 09 Jun 2024
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models Bálint Máté François Fleuret Tristan Bereau DiffM 18 2 0 04 Jun 2024
A Recipe for Charge Density Prediction Xiang Fu Andrew S. Rosen Kyle Bystrom Rui Wang Albert Musaelian Boris Kozinsky Tess E. Smidt Tommi Jaakkola 26 5 0 29 May 2024
E(n) Equivariant Topological Neural Networks Claudio Battiloro Ege Karaismailoglu Mauricio Tec George Dasoulas Michelle Audirac Francesca Dominici 41 4 0 24 May 2024
Grappa -- A Machine Learned Molecular Mechanics Force Field Leif Seute Eric Hartmann Jan Stühmer Frauke Gräter 14 3 0 25 Mar 2024
Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators Benedikt Alkin Andreas Fürst Simon Schmid Lukas Gruber Markus Holzleitner Johannes Brandstetter PINN AI4CE 27 8 0 19 Feb 2024
On the Completeness of Invariant Geometric Deep Learning Models Zian Li Xiyuan Wang Shijia Kang Muhan Zhang 17 2 0 07 Feb 2024
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds Ilyes Batatia 11 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 11 19 0 03 Oct 2023
Uncovering Neural Scaling Laws in Molecular Representation Learning Dingshuo Chen Yanqiao Zhu Jieyu Zhang Yuanqi Du Zhixun Li Qiang Liu Shu Wu Liang Wang 8 15 0 15 Sep 2023
Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions Janosh Riebesell Rhys E. A. Goodall Philipp Benner Chiang Yuan Bowen Deng A. Lee Anubhav Jain Kristin A. Persson OOD 13 33 0 28 Aug 2023
Beyond MD17: the reactive xxMD dataset Zihan Pengmei Junyu Liu Yinan Shu 6 6 0 22 Aug 2023
SE(3) Equivariant Augmented Coupling Flows Laurence I. Midgley Vincent Stimper Javier Antorán Emile Mathieu Bernhard Schölkopf José Miguel Hernández-Lobato 15 22 0 20 Aug 2023
Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions Michael Scherbela Leon Gerard Philipp Grohs 18 5 0 15 Jul 2023
MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models Michael R. Maser Natasa Tagasovska Jae Hyeon Lee Andrew Watkins 17 0 0 20 Jun 2023
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules Haiyang Yu Meng Liu Youzhi Luo A. Strasser X. Qian Xiaoning Qian Shuiwang Ji 8 19 0 15 Jun 2023
FAENet: Frame Averaging Equivariant GNN for Materials Modeling Alexandre Duval Victor Schmidt A. Garcia Santiago Miret Fragkiskos D. Malliaros Yoshua Bengio David Rolnick 9 51 0 28 Apr 2023
Rigid Body Flows for Sampling Molecular Crystal Structures Jonas Köhler Michele Invernizzi P. D. Haan Frank Noé AI4CE 17 27 0 26 Jan 2023
Structure-based drug design with geometric deep learning Clemens Isert Kenneth Atz G. Schneider 14 104 0 19 Oct 2022
Hierarchical Learning in Euclidean Neural Networks Joshua A. Rackers P. Rao 15 1 0 10 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 16 105 0 21 Sep 2022
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 161 1,095 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 183 1,218 0 08 Jan 2021