scholarly journals Localizing Frustration in Proteins Using All-Atom Energy Functions

2019 ◽  
Vol 123 (21) ◽  
pp. 4497-4504 ◽  
Author(s):  
Justin Chen ◽  
Nicholas P. Schafer ◽  
Peter G. Wolynes ◽  
Cecilia Clementi
Keyword(s):  
Energy Functions ◽  
Atom Energy

Data-Driven Many-Body Models with Chemical Accuracy for CH4/H2O Mixtures

2020 ◽  
Author(s):  
Marc Riera ◽  
Alan Hirales ◽  
Raja Ghosh ◽  
Francesco Paesani
Keyword(s):  
Liquid Phase ◽  
Quantitative Description ◽  
Liquid Mixtures ◽  
Many Body ◽  
Energy Functions ◽  
Potential Energy Functions ◽  
Dynamics Simulations ◽  
Body Potential ◽  
Small Clusters ◽  
Many Body Interactions

<div> <div> <div> <p>Many-body potential energy functions (PEFs) based on the TTM-nrg and MB-nrg theoretical/computational frameworks are developed from coupled cluster reference data for neat methane and mixed methane/water systems. It is shown that that the MB-nrg PEFs achieve subchemical accuracy in the representation of individual many-body effects in small clusters and enables predictive simulations from the gas to the liquid phase. Analysis of structural properties calculated from molecular dynamics simulations of liquid methane and methane/water mixtures using both TTM-nrg and MB-nrg PEFs indicates that, while accounting for polarization effects is important for a correct description of many-body interactions in the liquid phase, an accurate representation of short-range interactions, as provided by the MB-nrg PEFs, is necessary for a quantitative description of the local solvation structure in liquid mixtures. </p> </div> </div> </div>

Inspection of free energy functions in gradient crystal plasticity

2013 ◽  
Vol 29 (6) ◽  
pp. 763-772 ◽  
Author(s):  
Samuel Forest ◽  
Nicolas Guéninchault
Keyword(s):  
Free Energy ◽  
Crystal Plasticity ◽  
Energy Functions

scholarly journals Convexity and Stiffness in Energy Functions for Electrostatic Simulations

2015 ◽  
Vol 11 (4) ◽  
pp. 1419-1427 ◽  
Author(s):  
Justine S. Pujos ◽  
A. C. Maggs
Keyword(s):  
Energy Functions

Potential energy functions for the ground state surfaces of SO2 and S2O

1985 ◽  
Vol 56 (4) ◽  
pp. 839-851 ◽  
Author(s):  
J.N. Murrell ◽  
W. Craven ◽  
M. Vincent ◽  
Z.H. Zhu
Keyword(s):  
Potential Energy ◽  
Ground State ◽  
Energy Functions ◽  
Potential Energy Functions

scholarly journals Maximally Reliable Markov Chains Under Energy Constraints

2009 ◽  
Vol 21 (7) ◽  
pp. 1863-1912 ◽  
Author(s):  
Sean Escola ◽  
Michael Eisele ◽  
Kenneth Miller ◽  
Liam Paninski
Keyword(s):  
Physical System ◽  
Infinite Length ◽  
Energy Functions ◽  
Available Energy ◽  
Energy Demands ◽  
Multistate System ◽  
Linear Chains ◽  
Energy Constraints ◽  
Cascade Structure ◽  
Reliable Solution

Signal-to-noise ratios in physical systems can be significantly degraded if the outputs of the systems are highly variable. Biological processes for which highly stereotyped signal generations are necessary features appear to have reduced their signal variabilities by employing multiple processing steps. To better understand why this multistep cascade structure might be desirable, we prove that the reliability of a signal generated by a multistate system with no memory (i.e., a Markov chain) is maximal if and only if the system topology is such that the process steps irreversibly through each state, with transition rates chosen such that an equal fraction of the total signal is generated in each state. Furthermore, our result indicates that by increasing the number of states, it is possible to arbitrarily increase the reliability of the system. In a physical system, however, an energy cost is associated with maintaining irreversible transitions, and this cost increases with the number of such transitions (i.e., the number of states). Thus, an infinite-length chain, which would be perfectly reliable, is infeasible. To model the effects of energy demands on the maximally reliable solution, we numerically optimize the topology under two distinct energy functions that penalize either irreversible transitions or incommunicability between states, respectively. In both cases, the solutions are essentially irreversible linear chains, but with upper bounds on the number of states set by the amount of available energy. We therefore conclude that a physical system for which signal reliability is important should employ a linear architecture, with the number of states (and thus the reliability) determined by the intrinsic energy constraints of the system.

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