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Huang, Jing-Ning; Lee, Ching-Yieh

doi:10.1103/physrevd.29.1017

Cited by 8 publications

(4 citation statements)

References 10 publications

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“…The maximum LE we found was for acetonitrile molecules at −0.65 kcal/mol•HA. The binding affinity of organic solvents to the protein surface is very weak, mM level, [30][31][32] so a value like ours appears more reasonable. Acetonitrile's LE is in keeping with values desired from fragment screening.…”

Section: Free Energy Calculations From Cosolvent Simulationsmentioning

confidence: 55%

Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations

Ghanakota

DasGupta

Carlson

2019

J. Chem. Inf. Model.

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In our recent efforts to map protein surfaces using mixed-solvent molecular dynamics (MixMD), 1 we were able to successfully capture active sites and allosteric sites within the top-four most occupied hotspots. In this study, we describe our approach for estimating the thermodynamic profile of the binding sites identified by MixMD. First, we establish a framework for calculating free energies from MixMD simulations, and we compare our approach to alternative methods. Second, we present a means to obtain a relative ranking of the binding sites by their configurational entropy. The theoretical maximum and minimum free energy and entropy values achievable under such a framework along with the limitations of the techniques are discussed. Using this approach, the free energy and relative entropy ranking of the top-four MixMD binding sites were computed and analyzed across our allosteric protein targets:

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Section: Free Energy Calculations From Cosolvent Simulationsmentioning

confidence: 55%

Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations

Ghanakota

DasGupta

Carlson

2019

J. Chem. Inf. Model.

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“…In this paper, we will generalize the adaptive scheme proposed in Ref. 1 to the case of chaotic neural networks with time-varying delay. By combining the dynamical error feedback theory and the adaptive control, a simple and analytical scheme is proposed for the synchronization of two coupled identical neural networks with timevarying delay based on the invariance principle of functional differential equations.…”

mentioning

confidence: 98%

Adaptive synchronization of neural networks with or without time-varying delay

Cao

2006

Chaos: An Interdisciplinary Journal of Nonlinear Science

321

178

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In this paper, based on the invariant principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the synchronization of almost all kinds of coupled identical neural networks with time-varying delay, which can be chaotic, periodic, etc. We do not assume that the concrete values of the connection weight matrix and the delayed connection weight matrix are known. We show that two coupled identical neural networks with or without time-varying delay can achieve synchronization by enhancing the coupling strength dynamically. The update gain of coupling strength can be properly chosen to adjust the speed of achieving synchronization. Also, it is quite robust against the effect of noise and simple to implement in practice. In addition, numerical simulations are given to show the effectiveness of the proposed synchronization method.

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“…The nematic transition in bulk FeSe has a critical temperature [9] of T s = 90 K and the corresponding superconducting critical temperature [10] around T c = 8 K, which seems at first very low. However, thin films of this class of materials offer a unique opportunity to easily change T c and increase its value by at least one order of magnitude such as, e.g., in single layers of FeSe grown on SrTiO 3 [11][12][13][14]. This evidence probably indicates an unconventional mechanism at work in these materials.…”

Section: Introductionmentioning

confidence: 99%

Orbital selectivity versus Pomeranchuk instability in the iron–chalcogenide superconductors: A two-loop renormalization group study

Caetano

Freire

2019

Annals of Physics

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We perform a two-loop renormalization group (RG) analysis of a 2D effective multiband model, which is relevant for describing the low-energy properties of some iron-chalcogenide superconducting materials. Crucial ingredients in this analysis are the calculation of higher-order contributions in the RG scheme that go beyond the widely-used parquet approximation and the consequent inclusion of nontrivial self-energy effects of the model that yield an anisotropic renormalization of the quasiparticle weight in the system. The motivation of our work is the experimental discovery by Sprau et al. [Science 357, 75 (2017)] that orbitally-selective renormalization of the quasiparticle weight in the Hund's metal phase at moderate temperatures underpins the highly unusual gap in the superconducting phase of the FeSe compound at lower temperatures. One prediction we arrive here is that the underlying origin of nematicity in these systems may indeed come from orbitalselectivity, instead of a Pomeranchuk instability in the d± channel. This orbital selectivity is driven by the presence of stripe-type antiferromagnetic fluctuations in the model. Therefore, we argue that the present RG results may provide a scenario from a weak-to-moderate coupling perspective, in which the role of orbital selectivity to describe the physical properties of some iron-chalcogenide superconductors is emphasized.

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Decayπ+→e+e+e

Cited by 8 publications

References 10 publications

Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations

Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations

Adaptive synchronization of neural networks with or without time-varying delay

Orbital selectivity versus Pomeranchuk instability in the iron–chalcogenide superconductors: A two-loop renormalization group study

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