Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence

24 December 2022

Papers citing "Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence"

7 / 7 papers shown

Title
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression Pratik Rathore Zachary Frangella Madeleine Udell Michał Dereziński Madeleine Udell 42 1 0 14 Jul 2024
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields J. Frank Oliver T. Unke Klaus-Robert Muller Stefan Chmiela 26 3 0 21 Sep 2023
Robust, randomized preconditioning for kernel ridge regression Mateo Díaz Ethan N. Epperly Zachary Frangella J. Tropp R. Webber 34 11 0 24 Apr 2023
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 40 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 177 247 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 233 1,240 0 08 Jan 2021
Sharp analysis of low-rank kernel matrix approximations Francis R. Bach 86 277 0 09 Aug 2012