Predicting the early-stage creep dynamics of gels from their static structure by machine learning

Liu, Han; Xiao, Siqi; Tang, Longwen; Bao, Enigma; Li, Emily; Yang, Cheng‐Cheng; Zhao, Zhexin; Sant, Gaurav; Smedskjær, Morten Mattrup; Guo, Lei; Bauchy, Mathieu

doi:10.1016/j.actamat.2021.116817

Cited by 24 publications

(34 citation statements)

References 42 publications

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“…We also observe an increasing probability of Al 5 to decrease CN in a-Al 2 O 3 samples quenched at higher pressure, suggesting that this bond switching event of Al 5 is determined by the softness of the whole system. Based on the results of ref the energy barrier of DC activity becomes lower with increasing quenching pressure. Interestingly, as ⟨ S ⟩ of the whole system decreases (for increasing quenching pressure), the dependence of P R of Al 5 to decrease its CN on individual softness becomes less significant (Figure C).…”

Section: Results and Discussionmentioning

confidence: 96%

“…Although these theories have proposed the existence of defects in the glass structure, they are unfortunately phenomenological and fail to provide a precise definition of these defects, thus hindering their identification from first principles . Recently, several data-driven models have been proposed to forecast the propensity of particles or atoms to rearrange. − However, due to the structural complexity of amorphous materials, the correlations between the structure and fracture mechanism in oxide glasses remain unknown.…”

mentioning

confidence: 99%

“…Generally, the atoms with higher softness are more susceptible to rearrange or, equivalently, to be regarded as flow defects. The softness descriptor has been successfully adopted in understanding the dynamical heterogeneities at the glass transition, 22 creep dynamics of gels, 21 yielding behavior of disordered solids, 24 dynamics of grain boundaries in polycrystals, 25 thin film glass dynamics, 26 and crystallization kinetics at the solid−liquid interface. 27 Here, inspired by this approach, we explore whether the fracture behavior of amorphous oxides (which is governed by the local propensity of atoms to rearrange) is encoded in their static structure and whether the spontaneous dynamics (driven by temperature) has sufficient information encoded to predict the stress-driven dynamics during fracture.…”

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confidence: 99%

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Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning

Liu

Tang

et al. 2021

ACS Nano

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Thin films of amorphous alumina (a-Al2O3) have recently been found to deform permanently up to 100% elongation without fracture at room temperature. If the underlying ductile deformation mechanism can be understood at the nanoscale and exploited in bulk samples, it could help to facilitate the design of damage-tolerant glassy materials, the holy grail within glass science. Here, based on atomistic simulations and classification-based machine learning, we reveal that the propensity of a-Al2O3 to exhibit nanoscale ductility is encoded in its static (nonstrained) structure. By considering the fracture response of a series of a-Al2O3 systems quenched under varying pressure, we demonstrate that the degree of nanoductility is correlated with the number of bond switching events, specifically the fraction of 5- and 6-fold coordinated Al atoms, which are able to decrease their coordination numbers under stress. In turn, we find that the tendency for bond switching can be predicted based on a nonintuitive structural descriptor calculated based on the static structure, namely, the recently developed “softness” metric as determined from machine learning. Importantly, the softness metric is here trained from the spontaneous dynamics of the system (i.e., under zero strain) but, interestingly, is able to readily predict the fracture behavior of the glass (i.e., under strain). That is, lower softness facilitates Al bond switching and the local accumulation of high-softness regions leads to rapid crack propagation. These results are helpful for designing glass formulations with improved resistance to fracture.

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Section: Results and Discussionmentioning

confidence: 96%

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confidence: 99%

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confidence: 99%

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Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning

Liu

Tang

et al. 2021

ACS Nano

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“…A similar trend was reported in a recent work focused on softness analysis of colloidal gels under creep, where system average softness was found to depend on the strain rate. 41 Examining P R (S) as a function of σ c and r pos for both neutral and strong interactions reveals that the probability of rearrangement for a given softness varies significantly less with applied stress than with distance from the NP surface (see Figure S7). Our recent work also prove that the stress-enhanced dynamics in polymer glasses can be described by an Eyring-like model, which buttresses the validity of dynamical decomposition under creep deformation.…”

Section: Resultsmentioning

confidence: 99%

Understanding Creep Suppression Mechanism in Polymer Nanocomposites through Machine Learning

Yang¹,

Pressly²,

Natarajan³

et al. 2022

Preprint

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While recent efforts have shown how local structure plays an essential role in the dynamic heterogeneity of homogeneous glass-forming materials, systems containing interfaces such as thin films or composite materials remain poorly understood. It is known that interfaces perturb the molecular packing nearby, however, numerous studies show the dynamics are modified over a much larger range. Here, we examine the dynamics in polymer nanocomposites (PNCs) using a combination of simulations and experiments and quantitatively separate the role of polymer packing from other effects on the dynamics, as a function of distance from the nanoparticle surfaces. After showing good qualitative agreement between the simulations and experiments in glassy structure and creep compliance, we use a recently developed machine learning technique to decompose polymer dynamics in our simulated PNCs into structure-dependent and structureindependent processes. With this decomposition, the free energy barrier for polymer rearrangement can be described as a combination of packing-dependent and packing-independent barriers. We find both barriers are higher near nanoparticles and decrease with applied stress, quantitatively demonstrating that the slow interfacial dynamics is not solely due to polymer packing differences, but also the change of structure-dynamics relationships. Finally, we present how this decomposition can be used to accurately predict strain-time creep curves for PNCs from their static configuration, providing additional insights into the effects of polymernanoparticle interfaces on creep suppression in PNCs.

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“…[ 203 ] Alternatively, a nonintuitive structural metric termed “softness” was found to be strongly correlated with the dynamics of specific atoms based on their local structural environments, [ 204 ] and has been successfully linked with creep dynamics and yielding behavior of disordered materials. [ 205,58 ] Very recently, it has also been shown that this metric can be trained from the spontaneous dynamics of glass under zero strain, and then used to predict the glass’ fracture propensity under strain. [ 206 ] In particular, rapid crack propagation occurs upon the local accumulation of high‐softness regions.…”

Section: Emerging Glass Types With Optical Transparency and Tailored ...mentioning

confidence: 99%

Advancing the Mechanical Performance of Glasses: Perspectives and Challenges

et al. 2022

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in terms of deciphering structure-property relationships and the nonaffine nature of glass mechanical behavior, of computational and computer-assisted methods for accelerated materials discovery, and of physicochemical insight at glass formation and synthesis in unconventional types of materials. Despite this progress, significant questions remain as to the fundamental role of structural heterogeneity and its consequences for the predictability of mechanical properties underlying the many applications of glass. Today's challenge is to translate new understanding of the physics of disorder, glass chemistry, and surface mechanics into tools that enable future glass products with adapted elasticity, strength, and toughness. We will therefore consider fundamental advances in relation to emerging glass applications. While the aforementioned physical insights have mostly been obtained by computational simulation of model systems, and often through the examination of metallic glasses, we will here focus on glasses suitable for visible transparency, in particular silicate glasses, such as a representative of today's most prolific glass devices, ranging from ultrathin substrates to strong and visually transparent cover materials. We will further consider hybrid glasses and glass-like composite materials as emerging alternatives that may overcome the ubiquitous conflict between strength and toughness. [1] Glasses are materials that lack a crystalline microstructure and long-range atomic order. Instead, they feature heterogeneity and disorder on superstructural scales, which have profound consequences for their elastic response, material strength, fracture toughness, and the characteristics of dynamic fracture. These structure-property relations present a rich field of study in fundamental glass physics and are also becoming increasingly important in the design of modern materials with improved mechanical performance. A first step in this direction involves glass-like materials that retain optical transparency and the haptics of classical glass products, while overcoming the limitations of brittleness. Among these, novel types of oxide glasses, hybrid glasses, phase-separated glasses, and bioinspired glass-polymer composites hold significant promise. Such materials are designed from the bottom-up, building on structure-property relations, modeling of stresses and strains at relevant length scales, and machine learning predictions. Their fabrication requires a more scientifically driven approach to materials design and processing, building on the physics of structural disorder and its consequences for structural rearrangements, defect initiation, and dynamic fracture in response to mechanical load. In this article, a perspective is provided on this highly interdisciplinary field of research in terms of its most recent challenges and opportunities.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.202109029.

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Predicting the early-stage creep dynamics of gels from their static structure by machine learning

Cited by 24 publications

References 42 publications

Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning

Predicting Fracture Propensity in Amorphous Alumina from Its Static Structure Using Machine Learning

Understanding Creep Suppression Mechanism in Polymer Nanocomposites through Machine Learning

Advancing the Mechanical Performance of Glasses: Perspectives and Challenges

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