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

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...

Relations between Varshni and Morse potential energy parameters

Open Physics ◽  
2009 ◽  
Vol 7 (1) ◽  
Author(s):  
Teik-Cheng Lim ◽  
Rajendra Udyavara
Keyword(s):  
Potential Energy ◽  
Force Constant ◽  
Morse Potential ◽  
Morse Function ◽  
Energy Integral ◽  
Equal Energy ◽  
Molecular Force ◽  
Parameter Conversion ◽  
Bond Stretching ◽  
Small Change

AbstractA set of relationships between the Morse and Varshni potential functions for describing covalent bondstretching energy has been developed by imposing equal force constant and equal energy integral. In view of the extensive adoption of Morse function in molecular force fields, this paper suggests two sets of parameter conversions from Varshni to Morse. The parameter conversion based on equal force constant is applicable for small change in bond length, while the parameter conversion based on equal energy integral is more applicable for significant bond-stretching. Plotted results reveal that the Varshni potential function is more suitable for describing hard bonds rather than soft bonds.

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

Molecular Force Fields of Some Selenium and Telurium Hexahalide Ions

1970 ◽  
Vol 25 (4) ◽  
pp. 566-569
Author(s):  
M. N. Avasthi ◽  
M. L. Mehta
Keyword(s):  
Matrix Method ◽  
Force Fields ◽  
Force Constants ◽  
Mean Amplitudes Of Vibration ◽  
Molecular Force

Abstract Wilson's GF matrix method has been used to evaluate all the seven independent force constants of some XY6 type ions using Müller's mathematical constraint. Mean amplitudes of vibration and Bastiansen-Morino shrinkages have also been calculated for these ions.

scholarly journals [All]‐ S , S ‐dioxide Oligo‐Thienylenevinylenes: Synthesis and Structural/Electronic Shapes from Their Molecular Force Fields

2018 ◽  
Author(s):  
Cheryl Aharon ◽  
Fernando G. Guijarro ◽  
Samara Medina Rivero ◽  
Francisco J. Ramírez ◽  
Rubén Caballero ◽  
...  
Keyword(s):  
Force Fields ◽  
Molecular Force

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

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
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