Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size

20 April 2023

Papers citing "Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size"

24 / 24 papers shown

Title
Towards scientific machine learning for granular material simulations -- challenges and opportunities Marc Fransen Andreas Fürst D. Tunuguntla Daniel N. Wilke Benedikt Alkin ... Takayuki Shuku WaiChing Sun T. Weinhart Dongwei Ye Hongyang Cheng AI4CE 23 0 0 01 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 42 0 0 31 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 52 4 0 12 Mar 2025
Symmetry-Preserving Diffusion Models via Target Symmetrization Vinh Tong Yun Ye Trung-Dung Hoang Anji Liu Guy Van den Broeck Mathias Niepert DiffM 75 0 0 17 Feb 2025
Implicit Delta Learning of High Fidelity Neural Network Potentials Stephan Thaler Cristian Gabellini Nikhil Shenoy Prudencio Tossou AI4CE 73 0 0 08 Dec 2024
Learning Macroscopic Dynamics from Partial Microscopic Observations Mengyi Chen Qianxiao Li AI4CE 26 0 0 31 Oct 2024
Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day Jianxiong Li Boyang Li Zhuoqiang Guo Mingzhen Li Enji Li Lijun Liu Guojun Yuan Zhan Wang Guangming Tan Weile Jia AI4CE 25 1 0 30 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
Breaking the Molecular Dynamics Timescale Barrier Using a Wafer-Scale System Kylee Santos Stan Moore Tomas Oppelstrup Amirali Sharifian I. Sharapov ... S. Pakin Edgar A Leon J. Laros Michael James S. Rajamanickam AI4CE 25 3 0 13 May 2024
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning Bowen Deng Yunyeong Choi Peichen Zhong Janosh Riebesell Shashwat Anand Zhuohan Li KyuJung Jun Kristin A. Persson Gerbrand Ceder AI4CE 24 16 0 11 May 2024
Grappa -- A Machine Learned Molecular Mechanics Force Field Leif Seute Eric Hartmann Jan Stühmer Frauke Gräter 27 3 0 25 Mar 2024
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields Yi-Lun Liao Tess E. Smidt Abhishek Das DiffM AI4CE 25 10 0 14 Mar 2024
LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields Joshua A. Vita Amit Samanta Fei Zhou Vincenzo Lordi 16 2 0 01 Feb 2024
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics Shengchao Liu Weitao Du Yanjing Li Zhuoxinran Li Vignesh C. Bhethanabotla ... O. Yaghi C. Borgs A. Anandkumar Hongyu Guo J. Chayes AI4CE 32 4 0 26 Jan 2024
Navigating protein landscapes with a machine-learned transferable coarse-grained model N. Charron Felix Musil Andrea Guljas Yaoyi Chen Klara Bonneau ... B. Husic Ankit Patel Gianni de Fabritiis Frank Noé C. Clementi AI4CE 11 13 0 27 Oct 2023
Learning Interatomic Potentials at Multiple Scales Xiang Fu Albert Musaelian Anders Johansson Tommi Jaakkola Boris Kozinsky 27 2 0 20 Oct 2023
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite Stefania Costantini Gianluca Galletti Fabian Fritz Stefan Adami Nikolaus A. Adams 30 13 0 28 Sep 2023
Variational Monte Carlo on a Budget -- Fine-tuning pre-trained Neural Wavefunctions Michael Scherbela Leon Gerard Philipp Grohs 28 5 0 15 Jul 2023
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials Guillem Simeon Gianni de Fabritiis 16 43 0 10 Jun 2023
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science D. P. Kovács Ilyes Batatia E. Arany Gábor Csányi AI4CE 8 80 0 23 May 2023
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 24 105 0 21 Sep 2022
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations Oliver T. Unke M. Stohr Stefan Ganscha Thomas Unterthiner Hartmut Maennel ... Daniel Ahlin M. Gastegger L. M. Sandonas A. Tkatchenko Klaus-Robert Muller AI4CE 24 18 0 17 May 2022
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 142 242 0 01 May 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 188 1,218 0 08 Jan 2021