Structural signatures for thermodynamic stability in vitreous silica: Insight from machine learning and molecular dynamics simulations

Zheng, Yu; Liu, Qitong; Szlufarska, Izabela; Wang, Bu

doi:10.1103/physrevmaterials.5.015602

Cited by 10 publications

(5 citation statements)

References 49 publications

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“…These more stable glasses show distinct atomic structures and materials properties compared to glasses with higher cooling rates, such as higher density and higher mechanical strength. 57,58 Such a cooling rate effect is observed in the elastic regime of thermosets, as shown in Fig. 8A, as the elastic modulus and yield stress increase with reduced cooling rates (details summarized in Table S1 of the Supporting Information).…”

Section: Temperature Effectmentioning

confidence: 76%

Fast and accurate modeling of thermoset fracture by active learning quantum-chemical bond scission

Yu,

Jackson

2023

Preprint

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A molecular understanding of thermoset fracture is crucial for enhancing performance and durability across applications. However, achieving accurate atomistic modeling of thermoset fracture remains computationally prohibitive due to the high cost associated with quantum mechanical methods for describing bond breaking. In this work, we introduce an active learning (AL) framework for our recently developed machine-learning based adaptable bond topology (MLABT) model that uses datasets generated via density functional theory (DFT) calculations that are both minimalistic and informative. Employing MLABT integrated with AL and DFT, we explore fracture behavior in highly crosslinked thermosets, assessing the variations in fracture behavior induced by system temperature, temperature fluctuations, strain rate, cooling rate, and degree of crosslinking. Notably, we discover that while fracture is minimally affected by temperature, it is strongly influenced by strain rate, suggesting the absence of the time-temperature superposition in thermoset plasticity. Furthermore, while the structural disparities introduced by different network annealing rates influence the elastic properties, they are inconsequential for thermoset fracture. In contrast, network topology emerges as the dominant determinant of fracture, influencing both the ultimate strain and stress. The integration of MLABT with the AL framework paves the way for efficient and DFT-accurate modeling of thermoset fracture, providing an affordable and accurate approach for calculating polymer network fracture across chemical space.

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Section: Temperature Effectmentioning

confidence: 76%

Fast and accurate modeling of thermoset fracture by active learning quantum-chemical bond scission

Yu,

Jackson

2023

Preprint

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“…Specifically, a smaller cooling rate results in a lower glass-transition temperature and, thereby, a thermodynamically more stable glass state, i.e., a state located lower in the potential energy landscape. These more stable glasses show distinct atomic structures and material properties compared to glasses with higher cooling rates, such as higher density and higher mechanical strength. , Such a cooling rate effect is observed in the elastic regime of thermosets, as shown in Figure A, as the elastic modulus and yield stress increase with reduced cooling rates (details are summarized in Table S1 of the Supporting Information). Note that in the simulations, the initial structures have identical bonding topology but only are generated by different cooling rates in melt-quenching simulation initial configurations from 800 to 300 K prior to deformation.…”

Section: Resultsmentioning

confidence: 96%

Exploring Thermoset Fracture with a Quantum Chemically Accurate Model of Bond Scission

Yu,

Jackson

2024

Macromolecules

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A molecular understanding of thermoset fracture is crucial for enhancing the performance and durability across applications. However, achieving accurate atomistic modeling of thermoset fracture remains computationally prohibitive due to the high cost associated with quantum mechanical methods for describing bond breaking. In this work, we introduce an active learning (AL) framework for our recently developed machine learning-based adaptable bond topology (MLABT) model that uses data sets generated via density functional theory (DFT) calculations that are both minimalistic and informative. Employing MLABT integrated with AL and DFT, we explore fracture behavior in highly cross-linked thermosets, assessing the variations in fracture induced by system temperature, temperature fluctuations, strain rate, cooling rate, and degree of cross-linking. Notably, we discover that while fracture is minimally affected by temperature, it is strongly influenced by the strain rate. Furthermore, while the structural disparities introduced by different network annealing rates influence the elastic properties, they are inconsequential for thermoset fracture. In contrast, network topology emerges as the dominant determinant of fracture, influencing both the ultimate strain and stress. Particularly, MLABT with AL-DFT achieving near quantum-chemical bond breaking accuracy still leads to ductile failures, emphasizing the necessity of modeling polymer networks at larger length scales for bridging the gap between the experiment and simulation. Nevertheless, the integration of MLABT with the AL framework paves the way for efficient and DFT-accurate modeling of thermoset fracture, providing an affordable and accurate approach for calculating polymer network fracture across chemical space.

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“…With lowering temperature, the height of this peak decreases quadratically in the case of liquid silica (please refer to SI for more detailed analysis). [25] As a result of different temperature dependencies of the two microscopic dynamics channels, the averaged activation enthalpy shows a transition around 3100 K, as shown in Fig. 3d.…”

Section: B Activation Energetics Of Microscopic Dynamicsmentioning

confidence: 89%

“…Previous studies have already shown the concentration of certain SRO defects in silica has strong dependence on temperature (or inherent enthalpy). [25,29] Some of these defects, such as under or over coordinated Si, are structurally similar to vacancies and interstitials in quartz. [30] However, unlike in crystals where point defects and their formation energy are usually well defined, the SRO defects in glass structures are complex and may involve different levels and types of distortions without obvious coordination defects.…”

Section: Discussionmentioning

confidence: 99%

Understanding the fragile-to-strong transition in silica from microscopic dynamics

Yu,

Morgan,

Ediger

et al. 2022

Preprint

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In this work, we revisit the fragile-to-strong transition (FTS) in the simulated BKS silica from the perspective of microscopic dynamics in an effort to elucidate the dynamical behaviors of fragile and strong glass-forming liquids. Softness, which is a machine-learned feature from local atomic structures, is used to predict the microscopic activation energetics and long-term dynamics. The FTS is found to originate from a change in the temperature dependence of the microscopic activation energetics. Furthermore, results suggest there are two diffusion channels with different energy barriers in BKS silica. The fast dynamics at high temperatures is dominated by the channel with small energy barriers (<∼1 eV), which is controlled by the short-range order. The rapid closing of this diffusion channel when lowering temperature leads to the fragile behavior. On the other hand, the slow dynamics at low temperatures is dominated by the channel with large energy barriers controlled by the medium-range order. This slow diffusion channel changes only subtly with temperature, leading to the strong behavior. The distributions of barriers in the two channels show different temperature dependences, causing a crossover at ∼3100 K. This transition temperature in microscopic dynamics is consistent with the inflection point in the configurational entropy, suggesting there is a fundamental correlation between microscopic dynamics and thermodynamics.

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Structural signatures for thermodynamic stability in vitreous silica: Insight from machine learning and molecular dynamics simulations

Cited by 10 publications

References 49 publications

Fast and accurate modeling of thermoset fracture by active learning quantum-chemical bond scission

Fast and accurate modeling of thermoset fracture by active learning quantum-chemical bond scission

Exploring Thermoset Fracture with a Quantum Chemically Accurate Model of Bond Scission

Understanding the fragile-to-strong transition in silica from microscopic dynamics

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