Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science

23 May 2023

Papers citing "Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science"

21 / 21 papers shown

Title
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE J. Firoz Franco Pellegrini Mario Geiger Darren J. Hsu Jenna A. Bilbrey ... Chris Mundy Gábor Csányi Justin S. Smith Ponnuswamy Sadayappan Sutanay Choudhury 21 0 0 14 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 44 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
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 68 0 0 26 Feb 2025
Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science Fraser Birks Thomas D Swinburne James R Kermode 51 0 0 26 Feb 2025
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains Eric Qu Aditi S. Krishnapriyan LRM 18 10 0 31 Oct 2024
On the design space between molecular mechanics and machine learning force fields Yuanqing Wang Kenichiro Takaba Michael S. Chen Marcus Wieder Yuzhi Xu ... Kyunghyun Cho Joe G. Greener Peter K. Eastman Stefano Martiniani M. Tuckerman AI4CE 32 4 0 03 Sep 2024
Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications Sanjif Shanmugavelu Mathieu Taillefumier Christopher Culver Oscar Hernandez Mark Coletti Ada Sedova 25 1 0 09 Aug 2024
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing Viktor Zaverkin Francesco Alesiani Takashi Maruyama Federico Errica Henrik Christiansen Makoto Takamoto Nicolas Weber Mathias Niepert 36 5 0 23 May 2024
Accurate Crystal Structure Prediction of New 2D Hybrid Organic Inorganic Perovskites Nima Karimitari William J. Baldwin Evan W. Muller Zachary J. L. Bare W. J. Kennedy Gábor Csányi Christopher Sutton 23 5 0 11 Mar 2024
On the Completeness of Invariant Geometric Deep Learning Models Zian Li Xiyuan Wang Shijia Kang Muhan Zhang 31 2 0 07 Feb 2024
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products Shengjie Luo Tianlang Chen Aditi S. Krishnapriyan 22 19 0 18 Jan 2024
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 25 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 17 30 0 25 Oct 2023
Equivariant Matrix Function Neural Networks Ilyes Batatia Lars L. Schaaf Huajie Chen Gábor Csányi Christoph Ortner Felix A. Faber 13 5 0 16 Oct 2023
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials Guillem Simeon Gianni de Fabritiis 19 43 0 10 Jun 2023
Wigner kernels: body-ordered equivariant machine learning without a basis Filippo Bigi Sergey Pozdnyakov Michele Ceriotti 19 15 0 07 Mar 2023
Tensor-reduced atomic density representations James P. Darby D. P. Kovács Ilyes Batatia M. A. Caro G. Hart Christoph Ortner Gábor Csányi 36 32 0 02 Oct 2022
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs Yi-Lun Liao Tess E. Smidt 75 211 0 23 Jun 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 188 1,218 0 08 Jan 2021
Tensor Decomposition for Signal Processing and Machine Learning N. Sidiropoulos L. De Lathauwer Xiao Fu Kejun Huang Evangelos E. Papalexakis Christos Faloutsos 90 1,334 0 06 Jul 2016