scholarly journals Machine learning of correlated dihedral potentials for atomistic molecular force fields

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Pascal Friederich ◽  
Manuel Konrad ◽  
Timo Strunk ◽  
Wolfgang Wenzel
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields

2019 ◽  
Vol 59 (10) ◽  
pp. 4278-4288 ◽  
Author(s):  
James L. McDonagh ◽  
Ardita Shkurti ◽  
David J. Bray ◽  
Richard L. Anderson ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Coarse Grained ◽  
Molecular Force

scholarly journals sGDML: Constructing accurate and data efficient molecular force fields using machine learning

2019 ◽  
Vol 240 ◽  
pp. 38-45 ◽  
Author(s):  
Stefan Chmiela ◽  
Huziel E. Sauceda ◽  
Igor Poltavsky ◽  
Klaus-Robert Müller ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force

scholarly journals Machine learning of accurate energy-conserving molecular force fields

2017 ◽  
Vol 3 (5) ◽  
pp. e1603015 ◽  
Author(s):  
Stefan Chmiela ◽  
Alexandre Tkatchenko ◽  
Huziel E. Sauceda ◽  
Igor Poltavsky ◽  
Kristof T. Schütt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force ◽  
Energy Conserving

scholarly journals Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields

2020 ◽  
Vol 153 (12) ◽  
pp. 124109
Author(s):  
Huziel E. Sauceda ◽  
Michael Gastegger ◽  
Stefan Chmiela ◽  
Klaus-Robert Müller ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force ◽  
Gradient Domain

scholarly journals Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

2019 ◽  
Vol 150 (11) ◽  
pp. 114102 ◽  
Author(s):  
Huziel E. Sauceda ◽  
Stefan Chmiela ◽  
Igor Poltavsky ◽  
Klaus-Robert Müller ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Force Fields ◽  
Coupled Cluster ◽  
Molecular Force ◽  
Gradient Domain

scholarly journals Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

2021 ◽  
Vol 154 (12) ◽  
pp. 124102
Author(s):  
Gregory Fonseca ◽  
Igor Poltavsky ◽  
Valentin Vassilev-Galindo ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Unsupervised Machine Learning ◽  
Configurational Space ◽  
Molecular Force

Molecular force fields. Part XII.—Force constant relationships in the non-metallic hydrides

1950 ◽  
Vol 46 (0) ◽  
pp. 137-146 ◽  
Author(s):  
D. F. Heath ◽  
J. W. Linnett ◽  
P. J. Wheatley
Keyword(s):  
Force Constant ◽  
Force Fields ◽  
Molecular Force

Predictive abilities of scaled quantum-mechanical molecular force fields: application to 2-methylbuta-1,3-diene (isoprene)

2008 ◽  
Vol 19 (3) ◽  
pp. 421-428 ◽  
Author(s):  
Yurii N. Panchenko ◽  
Charles W. Bock ◽  
Joseph D. Larkin ◽  
Alexander V. Abramenkov ◽  
Frank Kühnemann
Keyword(s):  
Force Fields ◽  
Quantum Mechanical ◽  
Molecular Force
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