A review on Machine learning aspect in physics and mechanics of glasses

Singh, Jashanpreet; Singh, Simranjit

doi:10.1016/j.mseb.2022.115858

Cited by 23 publications

(8 citation statements)

References 186 publications

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“…As also shown in Table 2, ML has been successfully used to model glass properties, including glass transition temperature, 65 thermal expansion, 67 Young's modulus, 73 chemical durability, 56,57 crystallization propensity for phases, such as nepheline, 27,28 and structural properties, such as boron coordination. 81 Several review articles 18,[82][83][84] have been published examining the application of ML in the field of glass science. For instance, Liu et al 82 conducted a review of ML algorithms utilized in this area.…”

Section: Model Formsmentioning

confidence: 99%

Glass formulation and composition optimization with property models: A review

Vienna

2023

J Am Ceram Soc.

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Glass is a versatile material with a remarkable history and many practical applications. It plays a critical role in our everyday lives, the advancement of science, and the development of many technologies. The Edisonian type trial‐and‐error method was commonly used for conventional design of glass compositions, which was time‐consuming and costly. With the urgent need to develop new glass compositions for technology applications rapidly, it has become necessary to develop precise property models with predictive powers using large databases and efficient formulation approaches. This paper reviews the design of glass compositions using these analytical and numerical models of composition–structure–property relations of glasses, some based on large databases and machine learning approaches. Aspects of data collection, model fitting, feature extraction, model evaluation, and uncertainty quantification will be covered. Furthermore, advances in the glass optimization framework and available tools are summarized with examples. The outlook and perspective for further glass property model development and formulation approaches are discussed.

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Section: Model Formsmentioning

confidence: 99%

Glass formulation and composition optimization with property models: A review

Vienna

2023

J Am Ceram Soc.

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“…The most frequently used biopolymers are poly(lactide-co-glycolic), polycaprolactone, poly(lactic acid), and polyethylene for Mg (AZ31) alloy. There are several uses of poly(lactic acid) in the biomedical field, like tissue engineering, medication delivery, and 3D printing scaffolds [88]. As a result of this, medical devices also make use of it.…”

Section: Other Coatingsmentioning

confidence: 99%

Applications of biopolymer coatings in biomedical engineering

Singh

Gill

2022

J. Electrochem. Sci. Eng.

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In this paper, a comprehensive overview of recent developments in the many different types of biopolymer coatings used in biomedical applications is presented. Biopolymer coatings have tremendous promise in various biological applications due to their adaptability. Polymer coatings may be used to improve surface qualities to fulfill certain criteria or to include extra capabilities for various biomedical applications. This analysis focuses mostly on certain polymers for usage in coating applications as well as a variety of polymer coatings that provide enhanced functionalization. The most recent findings from relevant research have been presented on using polymer coatings on nanoparticles for biological applications. Moreover, the latest research on biopolymer coatings for improved tissue engineering and drug delivery on various substrates and nanoparticles has been reported. Additionally, the most recent scientific breakthroughs have been compiled.

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“…In addition, ZrO 2 is an essential transition metal-oxide that plays a significant role in the development of high-performance computer systems. The authors The Behler-Parrinello Neural Network (BPNN) may be employed in the molecular dynamics simulation of the O 2 vacancy diffusion since its accuracy is similar to simulations [81] based on density functional theory (DFT) [82].…”

Section: Biomedical Applications Of Zromentioning

confidence: 99%

Artificial intelligence in use of ZrO2 material in biomedical science

Singh

Verma

2022

J. Electrochem. Sci. Eng.

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The rapidly growing discipline of artificial intelligence (AI) seeks to develop software and computers that can do tasks that have historically required the intelligence of people. Machine learning (ML) is a subfield of AI that makes use of algorithms to "learn" from data's innate statistical patterns and structures to extrapolate information that is otherwise hidden. A growing emphasis on cosmetic dentistry has coincided with ZrO2‘s rise to prominence as a result of its improved biocompatibility, visually pleasant look, strong oxidation resistance, better mechanical properties, and lack of documented allergic responses. Advances in the field of AI and ML have led to novel applications of ZrO2 in dental devices for biological objectives. Artificial intelligence (AI) technologies have attracted a lot of attention in ZrO2-related research and therapeutic applications due to their ability to analyze data and discover connections between seemingly unrelated events. Specifically, their incorporation into zirconia is largely responsible for this. Zirconia's versatility in the scientific community means that how AI is used in the area varies with the specific directions in which zirconia is utilized. Therefore, this article primarily focuses on the use of AI in the biomedical use of ZrO2 in dentistry.

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A review on Machine learning aspect in physics and mechanics of glasses

Cited by 23 publications

References 186 publications

Glass formulation and composition optimization with property models: A review

Glass formulation and composition optimization with property models: A review

Applications of biopolymer coatings in biomedical engineering

Artificial intelligence in use of ZrO2 material in biomedical science

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