Martensite Start Temperature Predictor for Steels Using Ensemble Data Mining

Agrawal, Ankit; Saboo, Abhinav; Olson, G. B.; Choudhary, Alok

doi:10.1109/dsaa.2019.00067

Cited by 7 publications

(7 citation statements)

References 34 publications

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“…There are several ways of using the chemical formula-based representations for building ML/DL models, beginning with a simple vector of raw elemental fractions 140,141 or of weight percentages of alloying compositions [142][143][144][145] , as well as more sophisticated hand-crafted descriptors or physical attributes to add known chemistry knowledge (e.g., electronegativity, valency, etc. of constituent elements) to the feature representations [146][147][148][149][150][151] .…”

Section: Chemical Formula Representationmentioning

confidence: 99%

Recent advances and applications of deep learning methods in materials science

et al. 2022

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Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

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Section: Chemical Formula Representationmentioning

confidence: 99%

Recent advances and applications of deep learning methods in materials science

et al. 2022

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“…Further, we evaluated the performance of 19 algorithms available in Scikit-learn [ 79 ] with different hyper-parameters for the three different feature sets using the 10-fold cross-validation [ 80 ] to discover the model with highest accuracy and generalizability. The 75% train data is randomly split into ten subsets, and the model is fitted with nine subgroups and tested with the remaining subgroup [ 81 ]. After training and testing for 10 times, the average values of metrics such as the root mean square error (RMSE) and mean absolute error (MAE) were calculated using Eqs.…”

Section: Methodsmentioning

confidence: 99%

Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning

Wang

2020

Science and Technology of Advanced Materials

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Stacking fault energy (SFE) is of the most critical microstructure attribute for controlling the deformation mechanism and optimizing mechanical properties of austenitic steels, while there are no accurate and straightforward computational tools for modeling it. In this work, we applied both thermodynamic modeling and machine learning to predict the stacking fault energy (SFE) for more than 300 austenitic steels. The comparison indicates a high need of improving low-temperature CALPHAD (CALculation of PHAse Diagrams) databases and interfacial energy prediction to enhance thermodynamic model reliability. The ensembled machine learning algorithms provide a more reliable prediction compared with thermodynamic and empirical models. Based on the statistical analysis of experimental results, only Ni and Fe have a moderate monotonic influence on SFE, while many other elements exhibit a complex effect that their influence on SFE may change with the alloy composition.

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“…There are several ways of using the chemical formula based representations for building ML/DL models, beginning with a simple vector of raw elemental fractions [150,151] or of weight percentages of alloying compositions [152][153][154][155], as well as more sophisticated hand-crafted descriptors or physical attributes to add known chemistry knowledge (e.g. electronegativity, valency, etc.…”

Section: Chemical Formula Representationmentioning

confidence: 99%

Recent Advances and Applications of Deep Learning Methods in Materials Science

Choudhary,

DeCost,

Chen

et al. 2021

Preprint

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Martensite Start Temperature Predictor for Steels Using Ensemble Data Mining

Cited by 7 publications

References 34 publications

Recent advances and applications of deep learning methods in materials science

Recent advances and applications of deep learning methods in materials science

Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning

Recent Advances and Applications of Deep Learning Methods in Materials Science

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