Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics

11 April 2022

Papers citing "Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics"

48 / 48 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
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction Robert Appleton Brian C Barnes Alejandro Strachan FedML AI4CE 37 0 0 09 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 41 0 0 04 Apr 2025
Force-Free Molecular Dynamics Through Autoregressive Equivariant Networks Fabian L. Thiemann Thiago Reschützegger Massimiliano Esposito Tseden Taddese Juan D. Olarte-Plata Fausto Martelli AI4CE 52 0 0 31 Mar 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 59 4 0 12 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
Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors Qiangqiang Gu S. K. Pandey Zhanghao Zhouyin 63 0 0 02 Feb 2025
Learning local equivariant representations for quantum operators Zhanghao Zhouyin Zixi Gan MingKang Liu S. K. Pandey Linfeng Zhang Qiangqiang Gu 93 3 0 28 Jan 2025
NeuralDEM -- Real-time Simulation of Industrial Particulate Flows Benedikt Alkin Tobias Kronlachner Samuele Papa Stefan Pirker Thomas Lichtenegger Johannes Brandstetter PINN AI4CE 57 1 1 14 Nov 2024
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics Paul Fuchs Stephan Thaler Sebastien Röcken Julija Zavadlav DiffM 82 6 0 28 Aug 2024
Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto Viktor Zaverkin Mathias Niepert AI4CE 60 0 0 23 Jul 2024
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning Tianlang Chen Shengjie Luo Di He Shuxin Zheng Tie-Yan Liu Liwei Wang AI4CE 41 5 0 24 Jun 2024
Molecule Graph Networks with Many-body Equivariant Interactions Zetian Mao Jiawen Li Chen Liang Diptesh Das Masato Sumita Koji Tsuda Kelin Xia Koji Tsuda 35 1 0 19 Jun 2024
Machine learning Hubbard parameters with equivariant neural networks M. Uhrin A. Zadoks Luca Binci Nicola Marzari I. Timrov 33 6 0 04 Jun 2024
AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion Adeesh Kolluru John R. Kitchin DiffM 47 4 0 07 May 2024
FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials Thomas Plé Olivier Adjoua Louis Lagardère Jean‐Philip Piquemal 44 8 0 02 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
On the Completeness of Invariant Geometric Deep Learning Models Zian Li Xiyuan Wang Shijia Kang Muhan Zhang 48 2 0 07 Feb 2024
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
H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing Gian Marco Visani William Galvin Michael N. Pun Armita Nourmohammad 30 6 0 15 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
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions Alvaro Carbonero Alexandre Duval Victor Schmidt Santiago Miret Alex Hernandez-Garcia Yoshua Bengio David Rolnick 38 0 0 10 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 41 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
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite Stefania Costantini Gianluca Galletti Fabian Fritz Stefan Adami Nikolaus A. Adams 40 13 0 28 Sep 2023
Beyond MD17: the reactive xxMD dataset Zihan Pengmei Junyu Liu Yinan Shu 26 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 38 23 0 20 Aug 2023
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules Haiyang Yu Meng Liu Youzhi Luo A. Strasser X. Qian Xiaoning Qian Shuiwang Ji 21 20 0 15 Jun 2023
Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning Xiangzhe Kong Wen-bing Huang Yang Liu 25 13 0 02 Jun 2023
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size Albert Musaelian A. Johansson Simon L. Batzner Boris Kozinsky 36 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 34 33 0 07 Apr 2023
On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods Artur P. Toshev Ludger Paehler A. Panizza Nikolaus A. Adams AI4CE PINN 19 5 0 31 Mar 2023
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs Saro Passaro C. L. Zitnick 3DPC 37 80 0 07 Feb 2023
Implicit Convolutional Kernels for Steerable CNNs Maksim Zhdanov Nico Hoffmann Gabriele Cesa 36 6 0 12 Dec 2022
SchNetPack 2.0: A neural network toolbox for atomistic machine learning Kristof T. Schütt Stefaan S. P. Hessmann Niklas W. A. Gebauer Jonas Lederer M. Gastegger 25 59 0 11 Dec 2022
Graph Contrastive Learning for Materials Teddy Koker Keegan Quigley Will Spaeth Nathan C. Frey Lin Li 19 5 0 24 Nov 2022
General time-reversal equivariant neural network potential for magnetic materials Hongyu Yu Boyu Liu Yang Zhong Liangliang Hong Jun-zhong Ji Changsong Xu X. Gong Hongjun Xiang 20 2 0 21 Nov 2022
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials Albert J. W. Zhu Simon L. Batzner Albert Musaelian Boris Kozinsky 22 45 0 17 Nov 2022
Learning the shape of protein micro-environments with a holographic convolutional neural network Michael N. Pun Andrew Ivanov Quinn Bellamy Zachary Montague Colin H. LaMont P. Bradley J. Otwinowski Armita Nourmohammad 19 12 0 05 Nov 2022
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science Santiago Miret Kin Long Kelvin Lee Carmelo Gonzales Marcel Nassar Matthew Spellings 38 19 0 31 Oct 2022
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations Xiang Fu Zhenghao Wu Wujie Wang T. Xie S. Keten Rafael Gómez-Bombarelli Tommi Jaakkola 32 138 0 13 Oct 2022
e3nn: Euclidean Neural Networks Mario Geiger Tess E. Smidt 46 173 0 18 Jul 2022
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts Richard Tran Janice Lan Muhammed Shuaibi Brandon M. Wood Siddharth Goyal ... Jehad Abed Oleksandr Voznyy Edward H. Sargent Zachary W. Ulissi C. L. Zitnick 36 174 0 17 Jun 2022
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields Ilyes Batatia D. P. Kovács G. Simm Christoph Ortner Gábor Csányi 47 442 0 15 Jun 2022
Automatic Identification of Chemical Moieties Jonas Lederer M. Gastegger Kristof T. Schütt Michael C. Kampffmeyer Klaus-Robert Muller Oliver T. Unke 21 5 0 30 Mar 2022
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 233 1,240 0 08 Jan 2021