Interatomic potentials for oxide glasses: Past, present, and future

Pedone, Alfonso; Bertani, Marco; Brugnoli, Luca; Pallini, Annalisa

doi:10.1016/j.nocx.2022.100115

Cited by 11 publications

(12 citation statements)

References 247 publications

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“…The choice of the potential depends on the system, on the properties of interest, and on the accuracy and transferability of the available force fields. 33 In literature, several comparative studies can be found that assess the performance of different potentials in the reproduction of the structure and properties of different glass compositions. 28,29,34,35 Potentials for borate and borosilicate glass systems have been introduced only recently, 27,[35][36][37][38][39][40][41][42] and to correctly reproduce the boron anomaly, that is, the dependence of boron coordination with composition, composition dependent parameters for B-O interactions are often required.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, empirical potentials are first validated on the reproduction of known experimental data before the calculated structures can be trustfully used. The choice of the potential depends on the system, on the properties of interest, and on the accuracy and transferability of the available force fields 33 . In literature, several comparative studies can be found that assess the performance of different potentials in the reproduction of the structure and properties of different glass compositions 28,29,34,35 …”

Section: Introductionmentioning

confidence: 99%

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Effects of magnesium on the structure of aluminoborosilicate glasses: NMR assessment of interatomic potentials models for molecular dynamics

et al. 2023

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Classical molecular dynamics simulations have been used to investigate the structural role of Mg and its effect when it is incorporated in sodium aluminoborosilicate glasses. The simulations have been performed using three interatomic potentials; one is based on the rigid ionic model parameterized by Wang et al. (2018) and two slightly different parameterization of the core–shell model provided by Stevensson et al. (2018) and Pedone et al. (2020) The accuracies of these models have been assessed by detailed structural analysis and comparing the simulated nuclear magnetic resonance (NMR) spectra for spin active nuclei (29Si, 27Al, 11B, 17O, 25Mg, and 23Na) with the experimental counterparts collected in a previous work. Our simulations reveal that the core–shell parameterizations provide better structural models. In fact, they better reproduce the NMR spectra of all the investigated nuclei and give better agreement with known experimental data. Magnesium is found to be five coordinated on average with distances with oxygen in between a network modifier (like Na) and an intermediate network formed (like Al). It prefers to lay closer to three‐coordinated B atoms, forming B–NBO bonds, with respect to Si and especially Al. This can explain the formation of AlO5 and AlO6 units in the investigated Na‐free glass, together with a Si clusterization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of magnesium on the structure of aluminoborosilicate glasses: NMR assessment of interatomic potentials models for molecular dynamics

et al. 2023

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“…Generally, the cycle performed in MD simulations is sketched in Figure 2 The functional used for calculating the force and relating acceleration may range from simple pairwise functionals to including a variety of complex multiatom correlations and/or bond angle restrictions. A number of force fields have been specially designed to replicate glass structure and properties [74,75].…”

Section: Atomistic Simulationsmentioning

confidence: 99%

“…Then, the molten state is simulated for some time to equilibrate the liquid before initiating cooling into the glassy state. While the simulation size and time is usually on the scale of thousands of atoms and several nanoseconds for classical MD, quantum mechanical MD is usually limited to few hundred atoms and some hundred picoseconds of simulation time [75,76]. This makes the cooling rates from the melt to the glassy state extreme compared to experiments.…”

Section: Atomistic Simulationsmentioning

confidence: 99%

“…However, for classical force fields, the parameters are usually parameterized to account for this difference to obtain a realistic structure [77]. While some highly successful parameterizations exist for specific glass compositions [75], the tranferability to other glass types is often limited, yet the flexibility of the MD method is a great advantage. As such, MD allows for performing a variety of studies including those purely dedicated to understanding structure, but given the access to atomic forces, studies of mechanical and thermodynamical properties are possible, as well as atomic scale resolution even under dynamic conditions or conditions for which the force field was not specifically made.…”

Section: Atomistic Simulationsmentioning

confidence: 99%

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Thermal conductivity of glasses: A structural point of view

Sørensen¹

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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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Interatomic potentials for oxide glasses: Past, present, and future

Cited by 11 publications

References 247 publications

Effects of magnesium on the structure of aluminoborosilicate glasses: NMR assessment of interatomic potentials models for molecular dynamics

Effects of magnesium on the structure of aluminoborosilicate glasses: NMR assessment of interatomic potentials models for molecular dynamics

Thermal conductivity of glasses: A structural point of view

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

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