Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks

Author(s):  
Nicodemo Di Pasquale ◽  
Joshua D. Elliott ◽  
Panagiotis Hadjidoukas ◽  
Paola Carbone
2019 ◽  
Author(s):  
Pier Paolo Poier ◽  
Louis Lagardere ◽  
Jean-Philip Piquemal ◽  
Frank Jensen

<div> <div> <div> <p>We extend the framework for polarizable force fields to include the case where the electrostatic multipoles are not determined by a variational minimization of the electrostatic energy. Such models formally require that the polarization response is calculated for all possible geometrical perturbations in order to obtain the energy gradient required for performing molecular dynamics simulations. </p><div> <div> <div> <p>By making use of a Lagrange formalism, however, this computational demanding task can be re- placed by solving a single equation similar to that for determining the electrostatic variables themselves. Using the recently proposed bond capacity model that describes molecular polarization at the charge-only level, we show that the energy gradient for non-variational energy models with periodic boundary conditions can be calculated with a computational effort similar to that for variational polarization models. The possibility of separating the equation for calculating the electrostatic variables from the energy expression depending on these variables without a large computational penalty provides flexibility in the design of new force fields. </p><div><div><div> </div> </div> </div> <p> </p><div> <div> <div> <p>variables themselves. Using the recently proposed bond capacity model that describes molecular polarization at the charge-only level, we show that the energy gradient for non-variational energy models with periodic boundary conditions can be calculated with a computational effort similar to that for variational polarization models. The possibility of separating the equation for calculating the electrostatic variables from the energy expression depending on these variables without a large computational penalty provides flexibility in the design of new force fields. </p> </div> </div> </div> </div> </div> </div> </div> </div> </div>


2021 ◽  
Author(s):  
Théo Jaffrelot Inizan ◽  
Frédéric Célerse ◽  
Olivier Adjoua ◽  
Dina El Ahdab ◽  
Luc-Henri Jolly ◽  
...  

We provide an unsupervised adaptive sampling strategy capable of producing μs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs).


2020 ◽  
Author(s):  
Richard T Bradshaw ◽  
Jacek Dziedzic ◽  
Chris-Kriton Skylaris ◽  
Jonathan W. Essex

<div><div><div><p>Preorganization of large, directionally oriented, electric fields inside protein active sites has been proposed as a crucial contributor to catalytic mechanism in many enzymes, and may be efficiently investigated at the atomistic level with molecular dynamics simulations. Here we evaluate the ability of the AMOEBA polarizable force field, as well as the additive Amber ff14SB and Charmm C36m models, to describe the electric fields present inside the active site of the peptidyl-prolyl isomerase cyclophilin A. We compare the molecular mechanical electric fields to those calculated with a fully first principles quantum mechanical (QM) representation of the protein, solvent, and ions, and find that AMOEBA consistently shows far greater correlation with the QM electric fields than either of the additive force fields tested. Catalytically-relevant fields calculated with AMOEBA were typically smaller than those observed with additive potentials, but were generally consistent with an electrostatically-driven mechanism for catalysis. Our results highlight the accuracy and the potential advantages of using polarizable force fields in systems where accurate electrostatics may be crucial for providing mechanistic insights.</p></div></div></div>


2019 ◽  
Vol 15 (4) ◽  
pp. 2444-2453 ◽  
Author(s):  
Vered Wineman-Fisher ◽  
Yasmine Al-Hamdani ◽  
Iqbal Addou ◽  
Alexandre Tkatchenko ◽  
Sameer Varma

2020 ◽  
Vol 276 ◽  
pp. 128262
Author(s):  
Xiaolong Chu ◽  
Yiming Zhang ◽  
Zhen Liu ◽  
Qing Huang ◽  
Xiaogang Lu ◽  
...  
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