Atomic Vibrations in Glasses

Hehlen, Bernard; Rufflé, Benoît

doi:10.1002/9781118801017.ch3.4

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…For example, the similarities between the Raman spectrum of vitreous silica and that of cristobalite or α-quartz are striking as illustrated in Fig. 20 [162]. One major difference, however, is the broad boson peak response appearing at low frequency in all vitreous materials.…”

Section: Inelastic Spectroscopies Of Lightmentioning

confidence: 94%

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Low-frequency vibrational spectroscopy of glasses

Rufflé¹,

Foret²,

Hehlen³

2021

Preprint

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Atomic vibrations in perfect, slightly defective or mixed crystals are to a large extent well understood since many decades. Theoretical descriptions are thus in excellent agreement with the experiments. As a consequence, phonon-related properties like specific heat, thermal conductivity or sound attenuation are also well explained in these solids. This is not yet the case in glasses where the lack of periodicity generates enormous difficulties in theoretical treatments as well as in experiments or in numerical simulations. Thanks to recent developments along all these lines, comprehensive studies have emerged in the last decades and several decisive advances have been made. This chapter is thus devoted to a discussion of the nature of the vibrational properties in glasses with particular emphasis on the low-frequency part of the vibrational density of states, including the acoustic excitations, and of the experimental techniques used to their study.

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Section: Inelastic Spectroscopies Of Lightmentioning

confidence: 94%

“…22b), and suggests that the IR response rather associates with the in-phase INS contribution. In silica, the HRS BP has been associated to local or quasi-local libration motions involving rigid SiO 4 units [162,175] introduced by Buchenau et al (see Fig. 17c).…”

Section: Glassmentioning

confidence: 99%

Low-frequency vibrational spectroscopy of glasses

Rufflé¹,

Foret²,

Hehlen³

2021

Preprint

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“…The spectral densities obtained via discrete Fourier-transform (DFT) are shown for some modes of a perfectly disordered configuration in Figure 2 (right). Hehlen and Rufflé [16] propose to characterize dispersion at least roughly by the mean wave number k ¯ and the standard deviation σ k 2 of the eigenvector spectral density. More specifically, the idea is to determine the graph ω = f ( k ¯ ) and to assign to every point the standard deviation as a horizontal error bar.…”

Section: Normal Mode Analysismentioning

confidence: 99%

“…Localization of vibration in disordered chains is investigated explicitly, for instance, by Kolan et al [12], Allen and Kelner [13], and de Moura et al [14]. More recent investigations regarding dispersion properties and wave scattering are reported by Taraskin and Elliott [15] and Hehlen and Ruffle´ [16].…”

Section: Introductionmentioning

confidence: 99%

Vibrational analysis of harmonic oscillator chains with random topological constraints

Mühlich

2022

Mathematics and Mechanics of Solids

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The effect of topological disorder on the dynamic behaviour of topologically constrained oscillator chains is investigated through a normal mode analysis. A measure for mode localization based on the skewness of the density of energy states of normal modes is proposed, which correlates well with main configuration characteristics. Appropriate configuration sampling is achieved by employing the Ising model together with the Metropolis algorithm.

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Accelerating NMR Shielding Calculations Through Machine Learning Methods: Application to Magnesium Sodium Silicate Glasses

Bertani,

Pedone,

Faglioni

et al. 2024

ChemPhysChem

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In this work, we have applied the Kernel Ridge Regression (KRR) method using a Least Square Support Vector Regression (LSSVR) approach for the prediction of the NMR isotropic magnetic shielding (σiso) of active nuclei (17O, 23Na, 25Mg, and 29Si) in a series of (Mg, Na) – silicate glasses. The Machine Learning (ML) algorithm has been trained by mapping the local environment of each atom described by the Smooth Overlap of Atomic Position (SOAP) descriptor with isotropic chemical shielding values computed with DFT using the Gauge‐Included‐Projector‐Augmented‐Wave (GIPAW) approach. The influence of different training datasets generated through molecular dynamics simulations at various temperatures and with different inter‐atomic potentials has been tested and we demonstrate the importance of a wide exploration of the configurational space to enhance the transferability of the ML‐regressor. Finally, the trained ML‐regressor has been used to simulate the 29Si MAS NMR spectra of systems containing up to 20000 atoms by averaging hundreds of configurations extracted from classical MD simulations to account for thermal vibrations. This ML approach is a powerful tool for the interpretation of NMR spectra using relatively large systems at a fraction of the computational time required by quantum mechanical calculations which are of high computational cost.

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Atomic Vibrations in Glasses

Cited by 4 publications

References 44 publications

Low-frequency vibrational spectroscopy of glasses

Low-frequency vibrational spectroscopy of glasses

Vibrational analysis of harmonic oscillator chains with random topological constraints

Accelerating NMR Shielding Calculations Through Machine Learning Methods: Application to Magnesium Sodium Silicate Glasses

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