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

Du, Tianyu; Liu, Han; Tang, Longwen; Sørensen, Søren Strandskov; Bauchy, Mathieu; Smedskjær, Morten Mattrup

doi:10.1021/acsnano.1c05619

Cited by 30 publications

(11 citation statements)

References 59 publications

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“…Next, we therefore attempt to decipher how the spatial environment around the P 2 S 6 4– , P 2 S 7 4– , and PS 4 3– anions affect the lithium transport in the present glasses. To this end, we correlate the local structure and dynamics by using the “softness” ,− concept, which is based on classification-based machine learning to investigate the relationship between the mobility of lithium and the local environment in which it is located. Specifically, we use logistic regression to build a hyperplane (that separates “mobile” from “immobile” lithium ions) based on the static structure and corresponding rearrangement of each lithium atom at 300 K as obtained from the MD simulations to identify their mobility.…”

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confidence: 99%

Deciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning

Chen

Christensen

et al. 2023

ACS Energy Lett.

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Glasses such as lithium thiophosphates (Li2S-P2S5) show promise as solid electrolytes for batteries, but a poor understanding of how the disordered structure affects lithium transport properties limits the development of glassy electrolytes. To address this, we here simulate glassy Li2S-P2S5 electrolytes with varying fractions of polyatomic anion clusters, i.e., P2S6 4–, P2S7 4–, and PS4 3–, using classical molecular dynamics. Based on the determined variation in ionic conductivity, we use a classification-based machine-learning metric termed “softness”a structural fingerprint that is correlated to the atomic rearrangement probabilityto unveil the structural origin of lithium-ion mobility. The softness distribution of lithium ions is highly spatially correlated: that is, the “soft” (high mobility) lithium ions are predominantly found around PS4 3– units, while the “hard” (low mobility) ions are found around P2S6 4– units. We also show that soft lithium-ion migration requires a smaller energy barrier to be overcome relative to that observed for hard lithium-ion migration.

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confidence: 99%

Deciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning

Chen

Christensen

et al. 2023

ACS Energy Lett.

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“…The researchers recently identified that the nanoscale ductility propensity is inherent in the static (nonstrained) structure of amorphous Al 2 O 3 by utilizing atomistic simulations and classification-based machine learning techniques (Figure 21h). 120 The study introduced a novel "softness" metric derived from machine learning, which can predict bond switching tendencies based on the static structure. Intriguingly, this softness metric was trained using spontaneous system dynamics (i.e., under zero strain) but could effectively predict the fracture behavior of the glass (i.e., under strain).…”

Section: Mechanical Propertiesmentioning

confidence: 99%

Section: Theoretical Studies For Amorphous Nanomaterialsmentioning

confidence: 99%

Recent Progress of Amorphous Nanomaterials

Kang

Yang

et al. 2023

Chem. Rev.

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Amorphous materials are metastable solids with only short-range order at the atomic scale, which results from local intermolecular chemical bonding. The lack of long-range order typical of crystals endows amorphous nanomaterials with unconventional and intriguing structural features, such as isotropic atomic environments, abundant surface dangling bonds, highly unsaturated coordination, etc. Because of these features and the ensuing modulation in electronic properties, amorphous nanomaterials display potential for practical applications in different areas. Motivated by these elements, here we provide an overview of the unique structural features, the general synthetic methods, and the potential for applications covered by contemporary research in amorphous nanomaterials. Furthermore, we discussed the possible theoretical mechanism for amorphous nanomaterials, examining how the unique structural properties and electronic configurations contribute to their exceptional performance. In particular, the structural benefits of amorphous nanomaterials as well as their enhanced electrocatalytic, optical, and mechanical properties, thereby clarifying the structure−function relationships, are highlighted. Finally, a perspective on the preparation and utilization of amorphous nanomaterials to establish mature systems with a superior hierarchy for various applications is introduced, and an outlook for future challenges and opportunities at the frontiers of this rapidly advancing field is proposed.

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“…[ 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. As these studies suggest, advances in new oxide glass composition design are expected to come from coupling knowledge of nanoscale deformation processes with high‐throughput ML methods.…”

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

Cited by 30 publications

References 59 publications

Deciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning

Deciphering How Anion Clusters Govern Lithium Conduction in Glassy Thiophosphate Electrolytes through Machine Learning

Recent Progress of Amorphous Nanomaterials

Advancing the Mechanical Performance of Glasses: Perspectives and Challenges

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