A new correlation between the characteristics temperature and glass-forming ability for bulk metallic glasses

Long, Zhilin; Liu, Wei; Ming, Zhong; Zhang, Yun; Zhao, Mingshengzi; Liao, Guangkai; Chen, Zhuo

doi:10.1007/s10973-018-7050-0

Cited by 58 publications

(7 citation statements)

References 48 publications

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“…However, the size of the database for thermal and mechanical properties is considerately smaller. For example, only about 840 data points are available for T g , [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62] 840 for T x , [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62] 850 for liquidus temperature T l , [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62] 322 for Young's modulus E, 10,[63][6...…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Customized design of amorphous solids by generative deep learning

Shang,

Zhou,

Han

et al. 2024

TIMS

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<p>The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge, derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimental validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.</p>

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Section: Resultsmentioning

confidence: 99%

“…(C-E) The distributions of the characteristic temperatures for metallic glasses. Data is taken from Refs [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]. .…”

mentioning

confidence: 99%

Customized design of amorphous solids by generative deep learning

Shang,

Zhou,

Han

et al. 2024

TIMS

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“…Many researchers are primarily focused on gaining a more precise understanding of the accurate assessment of GFA in relation to T g and various other physical parameters. [41][42][43] For instance, the formation of bulk metallic glass materials is primarily determined by the reduced glass transition temperature (T rg = T g /T l , where T l represents the liquidus temperature), which serves as an indicator of the GFA. 44 However, the primary focus of this work is currently on exploring the key relationship between T g and ML features, and the connections between ML features and GFA will be investigated in more detail in future work.…”

Section: Resultsmentioning

confidence: 99%

Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses

Zhang,

Zhao,

Zhong

et al. 2023

Nanoscale

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“…The GFA can be characterized by the critical cooling rate ( R c ) or the critical diameter ( D max ) of an alloy, and a lower R c and a larger D max indicate a better GFA. Some empirical criteria for the GFA of alloys have been proposed in the past few decades − such as the supercooled liquid region Δ T x = T x – T g , the reduced glass transformation temperature T rg = T g / T l , etc., where T g is the glass transition temperature, T x is the onset crystallization temperature, and T l is the liquidus temperature. It is reported that the GFA of alloys is correlated to many factors, including their composition, thermodynamics, kinetics, topology, and other properties. − Therefore, the intuition-based criteria mentioned above usually result in low accuracy and a limited range of GFA prediction.…”

Section: Introductionmentioning

confidence: 99%

Does the GFA of Alloys Depend on the Atomic Size Ratio: A DT-Based ML Study

Pan

Jia

et al. 2023

Crystal Growth & Design

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The difficulty in the prediction of the glass-forming ability (GFA) of alloys hinders the development of new materials with excellent properties. The GFA of alloys is investigated by using three machine learning models based on a decision tree, with a dataset covering 1404 multicomponent alloys collected from peer-reviewed publications. With the composition (including 94 elements, C94), three characteristic temperatures (T3), and three geometric parameters (G3) as the input feature, the XGBoost model achieves the best prediction result {RMSE = 2.5635, R 2 = 0.7250}, improved by 11.20% compared with the latest published paper. Among seven possible input feature sets, "C94 + T3" achieves the best with {RMSE = 2.5250, R 2 = 0.7332} on the XGBoost model. Further analysis revealed that superimposing G3 on C94 always reduces XGBoost performance; therefore, the GFA of alloys is independent of the atomic size ratio; this is consistent with the fundamental principle of physics: the radii of atoms are determined by element types. A precision of 95.5% is obtained for binary alloys even with an incomplete feature set. Therefore, with "C94 + T3" input features, the XGBoost is generally reliable to effectively predict the GFA of all alloys.

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A new correlation between the characteristics temperature and glass-forming ability for bulk metallic glasses

Cited by 58 publications

References 48 publications

Customized design of amorphous solids by generative deep learning

Customized design of amorphous solids by generative deep learning

Data-driven machine learning prediction of glass transition temperature and the glass-forming ability of metallic glasses

Does the GFA of Alloys Depend on the Atomic Size Ratio: A DT-Based ML Study

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