ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2205.08306
  4. Cited By
Accurate Machine Learned Quantum-Mechanical Force Fields for
  Biomolecular Simulations

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

17 May 2022
Oliver T. Unke
M. Stohr
Stefan Ganscha
Thomas Unterthiner
Hartmut Maennel
S. Kashubin
Daniel Ahlin
M. Gastegger
L. M. Sandonas
A. Tkatchenko
Klaus-Robert Muller
    AI4CE
ArXivPDFHTML

Papers citing "Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations"

12 / 12 papers shown
Title
Navigating protein landscapes with a machine-learned transferable
  coarse-grained model
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
16
13
0
27 Oct 2023
From Peptides to Nanostructures: A Euclidean Transformer for Fast and
  Stable Machine Learned Force Fields
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
Stress and heat flux via automatic differentiation
Stress and heat flux via automatic differentiation
Marcel F. Langer
J. Frank
Florian Knoop
27
8
0
02 May 2023
Scaling the leading accuracy of deep equivariant models to biomolecular
  simulations of realistic size
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
Albert Musaelian
A. Johansson
Simon L. Batzner
Boris Kozinsky
32
48
0
20 Apr 2023
Heat flux for semi-local machine-learning potentials
Heat flux for semi-local machine-learning potentials
Marcel F. Langer
Florian Knoop
Christian Carbogno
Matthias Scheffler
M. Rupp
20
8
0
25 Mar 2023
Reconstructing Kernel-based Machine Learning Force Fields with
  Super-linear Convergence
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
Stefan Blücher
Klaus-Robert Muller
Stefan Chmiela
11
4
0
24 Dec 2022
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
Machine Learning Coarse-Grained Potentials of Protein Thermodynamics
Maciej Majewski
Adriana Pérez
Philipp Thölke
Stefan Doerr
N. Charron
T. Giorgino
B. Husic
C. Clementi
Frank Noé
Gianni De Fabritiis
AI4CE
21
70
0
14 Dec 2022
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
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
Structure-based drug design with geometric deep learning
Structure-based drug design with geometric deep learning
Clemens Isert
Kenneth Atz
G. Schneider
53
104
0
19 Oct 2022
Inverse design of 3d molecular structures with conditional generative
  neural networks
Inverse design of 3d molecular structures with conditional generative neural networks
Niklas W. A. Gebauer
M. Gastegger
Stefaan S. P. Hessmann
Klaus-Robert Muller
Kristof T. Schütt
AI4CE
192
166
0
10 Sep 2021
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and
  Nonlocal Effects
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
174
246
0
01 May 2021
Deep neural network solution of the electronic Schrödinger equation
Deep neural network solution of the electronic Schrödinger equation
J. Hermann
Zeno Schätzle
Frank Noé
149
446
0
16 Sep 2019
1