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"

50 / 65 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 90 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 50 0 0 16 Apr 2025
Machine learning interatomic potential can infer electrical response Peichen Zhong Dongjin Kim Daniel S. King Bingqing Cheng 32 1 0 07 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 33 0 0 04 Apr 2025
Optimal Invariant Bases for Atomistic Machine Learning Alice Allen Emily Shinkle Roxana Bujack Nicholas Lubbers 37 0 0 30 Mar 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 77 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 80 3 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 33 0 0 10 Jan 2025
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs Yuanchang Zhou Siyu Hu Chen Wang Lin-Wang Wang Guangming Tan Weile Jia AI4CE GNN 50 0 0 30 Dec 2024
The dark side of the forces: assessing non-conservative force models for atomistic machine learning Filippo Bigi Marcel F. Langer Michele Ceriotti AI4CE 91 7 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 52 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 37 0 0 11 Nov 2024
Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential Kentaro Hino Yuki Kurashige 34 0 0 31 Oct 2024
Relaxed Equivariance via Multitask Learning Ahmed A. A. Elhag T. Konstantin Rusch Francesco Di Giovanni Michael Bronstein 47 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 38 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 26 1 0 15 Oct 2024
Learning Equivariant Non-Local Electron Density Functionals Nicholas Gao Eike Eberhard Stephan Günnemann 28 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 41 1 0 03 Oct 2024
Deep Signature: Characterization of Large-Scale Molecular Dynamics Tiexin Qin Mengxu Zhu Chunyang Li Terry Lyons Hong Yan Haoliang Li 28 0 0 03 Oct 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto Viktor Zaverkin Mathias Niepert AI4CE 60 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 42 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 40 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 47 7 0 09 Jun 2024
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models Bálint Máté François Fleuret Tristan Bereau DiffM 40 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 50 5 0 29 May 2024
E(n) Equivariant Topological Neural Networks Claudio Battiloro Ege Karaismailoglu Mauricio Tec George Dasoulas Michelle Audirac Francesca Dominici 52 5 0 24 May 2024
AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion Adeesh Kolluru John R. Kitchin DiffM 42 4 0 07 May 2024
Grappa -- A Machine Learned Molecular Mechanics Force Field Leif Seute Eric Hartmann Jan Stühmer Frauke Gräter 29 3 0 25 Mar 2024
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications Jiaqi Han Jiacheng Cen Liming Wu Zongzhao Li Xiangzhe Kong ... Zhewei Wei Deli Zhao Yu Rong Wenbing Huang Wenbing Huang AI4CE 34 20 0 01 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 45 8 0 19 Feb 2024
On the Completeness of Invariant Geometric Deep Learning Models Zian Li Xiyuan Wang Shijia Kang Muhan Zhang 33 2 0 07 Feb 2024
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory Alexander Mathiasen Hatem Helal Paul Balanca Adam Krzywaniak Ali Parviz Frederik Hvilshoj Bla.zej Banaszewski Carlo Luschi Andrew William Fitzgibbon 40 3 0 06 Feb 2024
Equivariant Symmetry Breaking Sets YuQing Xie Tess E. Smidt 25 4 0 05 Feb 2024
Molecular Hypergraph Neural Networks Junwu Chen Philippe Schwaller GNN 41 10 0 20 Dec 2023
Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN Guang Yang Yuan-Bin Liu Lei Yang Bingyang Cao AI4CE 30 6 0 20 Nov 2023
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction Nima Shoghi Adeesh Kolluru John R. Kitchin Zachary W. Ulissi C. L. Zitnick Brandon M. Wood AI4CE 24 32 0 25 Oct 2023
A Geometric Insight into Equivariant Message Passing Neural Networks on Riemannian Manifolds Ilyes Batatia 20 0 0 16 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 28 37 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
Uncovering Neural Scaling Laws in Molecular Representation Learning Dingshuo Chen Yanqiao Zhu Jieyu Zhang Yuanqi Du Zhixun Li Qiang Liu Shu Wu Liang Wang 32 16 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 30 35 0 28 Aug 2023
Beyond MD17: the reactive xxMD dataset Zihan Pengmei Junyu Liu Yinan Shu 21 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 35 22 0 20 Aug 2023
Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions Michael Scherbela Leon Gerard Philipp Grohs 33 5 0 15 Jul 2023
MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models Michael R. Maser Natasa Tagasovska Jae Hyeon Lee Andrew Watkins 31 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 15 20 0 15 Jun 2023
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning Xiangzhe Kong Wen-bing Huang Yang Liu 22 13 0 02 Jun 2023
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