Coarse Graining of a Giant Molecular System: The Chromatin Fiber

Author(s):  
Guido Tiana ◽  
Luca Giorgetti
2018 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin S. Smith ◽  
Jerzy Leszczynski ◽  
Olexandr Isayev

<p>Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.</p>


2019 ◽  
Author(s):  
Rebecca Lindsey ◽  
Nir Goldman ◽  
Laurence E. Fried ◽  
Sorin Bastea

<p>The interatomic Chebyshev Interaction Model for Efficient Simulation (ChIMES) is based on linear combinations of Chebyshev polynomials describing explicit two- and three-body interactions. Recently, the ChIMES model has been developed and applied to a molten metallic system of a single atom type (carbon), as well as a non-reactive molecular system of two atom types at ambient conditions (water). Here, we continue application of ChIMES to increasingly complex problems through extension to a reactive system. Specifically, we develop a ChIMES model for carbon monoxide under extreme conditions, with built-in transferability to nearby state points. We demonstrate that the resulting model recovers much of the accuracy of DFT while exhibiting a 10<sup>4</sup>increase in efficiency, linear system size scalability and the ability to overcome the significant system size effects exhibited by DFT.</p>


2019 ◽  
Author(s):  
Javier Oller ◽  
David A. Sáez ◽  
Esteban Vöhringer-Martinez

<div><div><div><p>Local reactivity descriptors such as atom condensed Fukui functions are promising computational tools to study chemical reactivity at specific sites within a molecule. Their applications have been mainly focused on isolated molecules in their most stable conformation without considering the effects of the surroundings. Here, we propose to combine QM/MM Born-Oppenheimer molecular dynamics simulations to obtain the microstates (configurations) of a molecular system using different representations of the molecular environment and calculate Boltzmann weighted atom condensed local reac- tivity descriptors based on conceptual DFT. Our approach takes the conformational fluctuations of the molecular system and the polarization of its electron density by the environment into account allowing us to analyze the effect of changes in the molecular environment on reactivity. In this contribution, we apply the method mentioned above to the catalytic fixation of carbon dioxide by crotonyl-CoA carboxylase/reductase and study if the enzyme alters the reactivity of its substrate compared to an aqueous solution. Our main result is that the protein en- vironment activates the substrate by the elimination of solute-solvent hydrogen bonds from aqueous solution in the two elementary steps of the reaction mechanism: the nucleophilic attack of a hydride anion from NADPH on the α, β unsaturated thioester and the electrophilic attack of carbon dioxide on the formed enolate species.</p></div></div></div>


CrystEngComm ◽  
2021 ◽  
Vol 23 (16) ◽  
pp. 3006-3014
Author(s):  
Wen Qian

A strategy combining classic and reactive molecular dynamics is applied to find the coupling effect of interfacial interactions and free radical reactions during the initial thermal decomposition of fluoropolymer-containing molecular systems.


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