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. 2306.11375
  4. Cited By
Top-down machine learning of coarse-grained protein force-fields

Top-down machine learning of coarse-grained protein force-fields

20 June 2023
Carles Navarro
Maciej Majewski
Gianni de Fabritiis
    AI4CE
ArXivPDFHTML

Papers citing "Top-down machine learning of coarse-grained protein force-fields"

4 / 4 papers shown
Title
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein
  Thermodynamics
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
Antonio Mirarchi
Raúl P. Peláez
Guillem Simeon
Gianni de Fabritiis
23
3
0
26 Sep 2024
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
Paul Fuchs
Stephan Thaler
Sebastien Röcken
J. Zavadlav
DiffM
72
6
0
28 Aug 2024
Accurate machine learning force fields via experimental and simulation
  data fusion
Accurate machine learning force fields via experimental and simulation data fusion
Sebastien Röcken
J. Zavadlav
AI4CE
29
12
0
17 Aug 2023
Coarse Graining Molecular Dynamics with Graph Neural Networks
Coarse Graining Molecular Dynamics with Graph Neural Networks
B. Husic
N. Charron
Dominik Lemm
Jiang Wang
Adria Pérez
...
Yaoyi Chen
Simon Olsson
Gianni de Fabritiis
Frank Noé
C. Clementi
AI4CE
35
158
0
22 Jul 2020
1