Molecular dynamics simulation of the effect of cooling rate on the structure and properties of lithium disilicate glass

Sun, Wei; Dierolf, Volkmar; Jain, Himanshu

doi:10.1016/j.jnoncrysol.2021.120991

Cited by 11 publications

(2 citation statements)

References 47 publications

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“…It is worth highlighting that the metastable nature of glasses implies that their structure and properties strongly depend on the cooling rate used to produce them (history of the sample). − Because of the computational costs, dynamics simulations of systems containing Avogadro’s number of atoms are not feasible, and periodic boundary conditions must be used to avoid surface effects. For this reason, as well as the limited time scale of MD simulations, the system is subjected to superheating and supercooling.…”

Section: Resultsmentioning

confidence: 99%

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

Bertani,

Charpentier,

Faglioni

et al. 2024

J. Chem. Theory Comput.

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An accurate and transferable machine learning (ML) potential for the simulation of binary sodium silicate glasses over a wide range of compositions (from 0 to 50% Na2O) was developed. The potential energy surface is approximated by the sum of atomic energy contributions mapped by a neural network algorithm from the local geometry comprising information on atomic distances and angles with neighboring atoms using the DeePMD code [WangH. Wang, H. Comput. Phys. Commun.2018228178184]. Our model was trained on a large data set of total energies and atomic forces computed at the density functional theory level on structures extracted from classical molecular dynamics (MD) simulations performed at several temperatures from 300 to 3000 K. This allows for the generation of a robust and transferable ML potential applicable over the full compositional range of glass formability at different temperatures that outperforms the empirical potentials available in the literature in reproducing structures and properties such as bond angle distribution, total distribution functions, and vibrational density of state. The generality of the approach enables the future training of a potential with other or more elements allowing for simulations of structures, properties, and behavior of ternary and multicomponent oxide glasses with nearly ab initio accuracy at a fraction of the computational cost.

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Section: Resultsmentioning

confidence: 99%

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

Bertani,

Charpentier,

Faglioni

et al. 2024

J. Chem. Theory Comput.

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“…In this composition fluctuation model, the Li 2 O-rich region is regarded as more fragile compared with the SiO 2 -rich region, and the initial crystallization will initiate in the Li 2 O-rich region, resulting in not only the formation of Li 2 SiO 3 crystals but also the possibility of other metastable crystalline phases. Sun et al 96 studied the effect of cooling rate on the structure and properties of LS2 glass using MD simulations and suggested the formation of Li clusters at lower cooling rates. Their study suggests that the distribution state of composition fluctuations in LS2 glass changes depending on the cooling rate for its SCL, that is, a slower cooling rate, a larger amount of Li 2 O-rich region.…”

Section: Crystallization Of LI 2 O⋅2sio 2 Glassmentioning

confidence: 99%

Nanoscale composition fluctuations and crystallization process: Case study in Li₂O–SiO₂‐based glasses

Komatsu

Honma

2022

Int J of Appl Glass Sci

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Nanoscale composition fluctuations in Li2O–SiO2‐based glasses were analyzed and discussed from the data on the structure and crystallization process reported so far to enter deeply into the medium‐range ordered structure of multicomponent oxide glasses. Li2O is proposed to have a strong tendency for dynamical heterogeneous structure, that is, the formation of fragile Li2O‐rich regions with small SiO2 contents, resulting in the initial crystallization of metastable Li2SiO3 prior to the formation of stable Li2Si2O5 in Li2O–2SiO2‐based glasses. Li2O–Ga2O3/Nb2O5–SiO2 glasses are proposed to have nanoscale composition fluctuations of Li2O–Ga2O3/Nb2O5‐rich regions, resulting in the initial formation of LiGa5O8 and LiNbO3 nanocrystals. In Li2O–Al2O3–SiO2 glasses, the distribution width of composition fluctuations is proposed to be narrow, resulting in the initial crystallization of metastable β‐quartz solid solutions Li2O·Al2O3·nSiO2 (n = 6–8). Additive P2O5 and NiO leading to an enhanced nucleation are proposed to be present in fragile Li2O‐rich regions.

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A new design of SiO2–Na2O–Al2O3 glass–ceramic and determination of elastic modulus and density of states via molecular dynamics simulations based on density functional tight-binding calculations

Çelik

2022

J. Korean Ceram. Soc.

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Molecular dynamics simulation of the effect of cooling rate on the structure and properties of lithium disilicate glass

Cited by 11 publications

References 47 publications

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

Nanoscale composition fluctuations and crystallization process: Case study in Li₂O–SiO₂‐based glasses

A new design of SiO2–Na2O–Al2O3 glass–ceramic and determination of elastic modulus and density of states via molecular dynamics simulations based on density functional tight-binding calculations

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Molecular dynamics simulation of the effect of cooling rate on the structure and properties of lithium disilicate glass

Cited by 11 publications

References 47 publications

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

Accurate and Transferable Machine Learning Potential for Molecular Dynamics Simulation of Sodium Silicate Glasses

Nanoscale composition fluctuations and crystallization process: Case study in Li2O–SiO2‐based glasses

A new design of SiO2–Na2O–Al2O3 glass–ceramic and determination of elastic modulus and density of states via molecular dynamics simulations based on density functional tight-binding calculations

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Product

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Nanoscale composition fluctuations and crystallization process: Case study in Li₂O–SiO₂‐based glasses