Effects of setting temperatures in the parallel tempering Monte Carlo algorithm

Rozada, Ignacio; Aramon, Maliheh; Machta, Jonathan; Katzgraber, Helmut G.

doi:10.1103/physreve.100.043311

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…By setting the inverse temperatures at each replica appropriately such that replica exchange is not impeded by a large temperature difference [15,16], we can obtain simultaneously the free energy F n (b l ) and posterior density p(w|D, b l ) at each replica temperature.…”

Section: Proposed Methods a Exchange Monte Carlo (Emc) Methodsmentioning

confidence: 99%

Fast Bayesian Deconvolution using Simple Reversible Jump Moves

Okajima,

Nagata,

Okada

2020

Preprint

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We propose a Markov chain Monte Carlo-based deconvolution method designed to estimate the number of peaks in spectral data, along with the optimal parameters of each radial basis function. Assuming cases where the number of peaks is unknown, and a sweep simulation on all candidate models is computationally unrealistic, the proposed method efficiently searches over the probable candidates via trans-dimensional moves assisted by annealing effects from replica exchange Monte Carlo moves. Through simulation using synthetic data, the proposed method demonstrates its advantages over conventional sweep simulations, particularly in model selection problems. Application to a set of olivine reflectance spectral data with varying forsterite and fayalite mixture ratios reproduced results obtained from previous mineralogical research, indicating that our method is applicable to deconvolution on real data sets.

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Section: Proposed Methods a Exchange Monte Carlo (Emc) Methodsmentioning

confidence: 99%

Fast Bayesian Deconvolution using Simple Reversible Jump Moves

Okajima,

Nagata,

Okada

2020

Preprint

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“…We present the numerical results of the proposed learning methods using artificial datasets consisting of N = 100 data points. The data points were generated by parallel tempering [19][20][21] from a generative RBM with n = 10 and m = 100. The parameters of the generative RBM were independently drawn from a normal distribution with a mean of zero and a variance of σ 2 , and the sample spaces of the generative RBM were set to V = H = {−1, +1}.…”

Section: Experiments Using Artificial Datasetmentioning

confidence: 99%

Effective learning algorithm for restricted Boltzmann machines via spatial Monte Carlo integration

Sekimoto

Yasuda

2023

NOLTA

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Restricted Boltzmann machines (RBMs) are a type of statistical machine learning model used in various applications. However, training RBM models is computationally difficult owing to the requirement of calculating expectations that have a combinatorial explosion. We provide a new and effective learning algorithm based on spatial Monte Carlo integration, which is an extension of the standard Monte Carlo integration and can approximate such intractable expectations with high accuracy. The proposed method exhibited superior performance compared to the standard learning method, i.e., contrastive divergence, in terms of accuracy and learning speed. However, there were cases in which the proposed learning method exhibited reduced performances. Thus, we further demonstrate that a heuristic initialization of the learning parameters can suppress this degradation.

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“…Atomic charges of the Sg-CN surface were obtained through the restrained electrostatic potential (RESP) method. First, the preferred orientation of the enzyme over the substrate was revealed through parallel tempering Monte Carlo simulations [59,60]. Next, an all-atom MD simulation was performed for the entire system using the CHARMM36 force field [41] in the GROMACS [61] package.…”

Section: Self-assembled Monolayer-based Biosensorsmentioning

confidence: 99%

Atomistic Simulations of Functionalized Nano-Materials for Biosensors Applications

Dutta

Corni

Brancolini

2022

IJMS

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Nanoscale biosensors, a highly promising technique in clinical analysis, can provide sensitive yet label-free detection of biomolecules. The spatial and chemical specificity of the surface coverage, the proper immobilization of the bioreceptor as well as the underlying interfacial phenomena are crucial elements for optimizing the performance of a biosensor. Due to experimental limitations at the microscopic level, integrated cross-disciplinary approaches that combine in silico design with experimental measurements have the potential to present a powerful new paradigm that tackles the issue of developing novel biosensors. In some cases, computational studies can be seen as alternative approaches to assess the microscopic working mechanisms of biosensors. Nonetheless, the complex architecture of a biosensor, associated with the collective contribution from “substrate–receptor–analyte” conjugate in a solvent, often requires extensive atomistic simulations and systems of prohibitive size which need to be addressed. In silico studies of functionalized surfaces also require ad hoc force field parameterization, as existing force fields for biomolecules are usually unable to correctly describe the biomolecule/surface interface. Thus, the computational studies in this field are limited to date. In this review, we aim to introduce fundamental principles that govern the absorption of biomolecules onto functionalized nanomaterials and to report state-of-the-art computational strategies to rationally design nanoscale biosensors. A detailed account of available in silico strategies used to drive and/or optimize the synthesis of functionalized nanomaterials for biosensing will be presented. The insights will not only stimulate the field to rationally design functionalized nanomaterials with improved biosensing performance but also foster research on the required functionalization to improve biomolecule–surface complex formation as a whole.

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Effects of setting temperatures in the parallel tempering Monte Carlo algorithm

Cited by 13 publications

References 27 publications

Fast Bayesian Deconvolution using Simple Reversible Jump Moves

Fast Bayesian Deconvolution using Simple Reversible Jump Moves

Effective learning algorithm for restricted Boltzmann machines via spatial Monte Carlo integration

Atomistic Simulations of Functionalized Nano-Materials for Biosensors Applications

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