Analysis and evaluation of machine learning applications in materials design and discovery

Golmohammadi, Mahsa; Aryanpour, Masoud

doi:10.1016/j.mtcomm.2023.105494

Cited by 10 publications

(4 citation statements)

References 178 publications

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“…Choudhary et al [58] outlined advances in DL methods for atomistic simulations, materials imaging, and spectral analysis. In addition, Golmohammadi and Aryanpour [59] collected and organized the articles containing keywords such as ML and materials discovery from 2011-2022 and predicted the properties of more than thirty materials using ML and proposed four guidelines to help researchers develop better models. Kadilkar et al [60] discussed the progress of using ML to design materials with target properties and how ML can reduce the design space's dimensionality.…”

Section: The Development Of MLmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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Optoelectronic materials are essential for today's scientific and technological development, and machine learning provides new ideas and tools for their research. In this paper, we first summarize the development history of optoelectronic materials and how materials informatics drives the innovation and progress of optoelectronic materials and devices. Then, we introduce the development of machine learning and its general process in optoelectronic materials and describe the specific implementation methods. We focus on the cases of machine learning in several application scenarios of optoelectronic materials and devices, including the methods related to crystal structure, properties (defects, electronic structure) research, materials and devices optimization, material characterization, and process optimization. In summarizing the algorithms and feature representations used in different studies, it is noted that prior knowledge can improve optoelectronic materials design, research, and decision-making processes. Finally, the prospect of machine learning applications in optoelectronic materials is discussed, along with current challenges and future directions. This paper comprehensively describes the application value of machine learning in optoelectronic materials research and aims to provide reference and guidance for the continuous development of this field.

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Section: The Development Of MLmentioning

confidence: 99%

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Zhang 张,

Fu 付

2023

Chinese Phys. B

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“…As is well known, in recent years, machine learning technology has developed rapidly and has been widely applied in the field of materials research, including alloy design [32,33], microstructure recognition [34], performance prediction [35], and process optimization [36], as well as the applications mentioned earlier in material hot deformation behavior and constitutive modeling. Wang et al [37] used a machine learning algorithm based on singular value decomposition and deep neural networks to build metamodels for constitutive models, which not only assists in parameter fitting but also facilitates the understanding and analysis of constitutive models.…”

Section: Introductionmentioning

confidence: 99%

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

Pan,

Song,

Gao

et al. 2024

Processes

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The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded. In this work, the hot deformation behavior of a Ni-Cr-Mo steel was investigated by conducting isothermal compression tests using a Gleeble-3800 thermal simulator with deformation temperatures ranging from 800 °C to 1200 °C, strain rates ranging from 0.01 s−1 to 10 s−1, and deformations of 55%. To analyze the constitutive relation of the Ni-Cr-Mo steel at high temperatures, five machine learning algorithms were employed to predict the flow stress, namely, back-propagation artificial neural network (BP-ANN), Random Committee, Bagging, k-nearest neighbor (k-NN), and a library for support vector machines (libSVM). A comparative study between the experimental and the predicted results was performed. The results show that correlation coefficient (R), root mean square error (RMSE), mean absolute value error (MAE), mean square error (MSE), and average absolute relative error (AARE) obtained from the Random Committee on the testing set are 0.98897, 8.00808 MPa, 5.54244 MPa, 64.12927 MPa2 and 5.67135%, respectively, whereas the metrics obtained via other algorithms are all inferior to the Random Committee. It suggests that the Random Committee can predict the flow stress of the steel more effectively.

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“…Compared with traditional experimental research methods, it has more accurate and reliable data analysis and prediction capabilities. With these outstanding performances, it plays an important role in material property optimization and alloy design [7,8].…”

Section: Introduction *mentioning

confidence: 99%

Yield Stress and Elongation Prediction and Process Parameter Optimization of Two C-Mn Steel Based on XGBoost and Non-dominated Sorting Genetic Algorithm-IIization of C-Mn steel based on XGBoost algorithm

QIN,

ZHAO,

JIN

et al. 2024

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Mechanical properties are the key guidance for controlling the product quality of steel and optimizing its process. In this work, four machine learning (ML) algorithms (XGBoost, SVR, BP, and RBF) are used to establish a correlation model between chemical composition, process parameters, and mechanical properties for performance optimization. The ML model showing high prediction accuracy is selected to analyze the importance of the model parameters. The XGBoost model has the highest prediction accuracy, with both yield strength (YS) and elongation-to-failure (EL) achieving a prediction accuracy of over 0.9 on the test set. Subsequently, the (YS) and (EL) of typical steel grades SPHC and Q235B are optimized by combining the high-precision XGBoost model and NSGA-II multi-objective optimization strategy. A comprehensive mechanical performance evaluation index (CP and EV) was proposed to screen the optimal Pareto frontiers, and two types of steel with excellent performance were successfully selected.

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Analysis and evaluation of machine learning applications in materials design and discovery

Cited by 10 publications

References 178 publications

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

Applications and potentials of machine learning in optoelectronic materials research: An overview and perspectives

The Prediction of Flow Stress in the Hot Compression of a Ni-Cr-Mo Steel Using Machine Learning Algorithms

Yield Stress and Elongation Prediction and Process Parameter Optimization of Two C-Mn Steel Based on XGBoost and Non-dominated Sorting Genetic Algorithm-IIization of C-Mn steel based on XGBoost algorithm

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