Regularized ab initio molecular force fields for key biological molecules: melatonin and pyridoxal-5′-phosphate methylamine Shiff base (Vitamin B6)

2020 ◽  
pp. 1-18
Author(s):  
Gulnara M. Kuramshina ◽  
Igor V. Kochikov ◽  
Svetlana A. Sharapova
Keyword(s):  
Ab Initio ◽  
Vitamin B6 ◽  
Force Fields ◽  
Biological Molecules ◽  
Molecular Force ◽  
Shiff Base

scholarly journals Representations of molecular force fields. I. Ethane: Ab initio and model, harmonic and anharmonic

1975 ◽  
Vol 63 (11) ◽  
pp. 4750-4758 ◽  
Author(s):  
L. S. Bartell ◽  
Susan Fitzwater ◽  
Warren J. Hehre
Keyword(s):  
Ab Initio ◽  
Force Fields ◽  
Molecular Force

Stable numerical methods for determination of the molecular clusters force fields

2020 ◽  
Vol 28 (5) ◽  
pp. 621-631
Author(s):  
Gulnara M. Kuramshina ◽  
Alexander A. Zakharov
Keyword(s):  
Inverse Problem ◽  
Ab Initio ◽  
Water Clusters ◽  
Force Fields ◽  
Quantum Mechanical ◽  
Quantum Mechanical Calculations ◽  
Small Water ◽  
Molecular Force ◽  
The Given

AbstractThe inverse problem of molecular force fields calculation is considered within the theory of regularization. In our strategy, we choose the stabilizing matrix F^{0} as a result of quantum mechanical calculations. The solution of the inverse problem is finding a matrix 𝐹 which is the nearest by the chosen Euclidean norm to the given ab initio F^{0}. The optimized solution is referred to as regularized quantum mechanical force field (RQMFF). Regularizing algorithms of molecular force fields calculation based on the joint treatment of experimental and ab initio quantum mechanical data have been applied to the calculations of molecular force fields (matrices of force constants) for small water clusters (H2O)𝑛 (n=2,3).

scholarly journals An atomistic fingerprint algorithm for learning ab initio molecular force fields

2018 ◽  
Vol 148 (3) ◽  
pp. 034101 ◽  
Author(s):  
Yu-Hang Tang ◽  
Dongkun Zhang ◽  
George Em Karniadakis
Keyword(s):  
Ab Initio ◽  
Force Fields ◽  
Molecular Force ◽  
Fingerprint Algorithm

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...

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
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