Modeling the influence of the composition of refractory elements on the heat resistance of nickel alloys by a deep learning artificial neural network

Tarasov, Dmitry; Milder, O. B.; Tiagunov, Andrei G.

doi:10.1002/mma.7524

Cited by 1 publication

(1 citation statement)

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“…To enhance the performance of these alloys, the addition of refractory elements is a common practice. Common refractory elements include W, Mo, Nb, and Ta, which raise the alloy’s melting point and thermal stability, thereby increasing its life span under high-temperature conditions. − However, the incorporation of these alloying elements can lead to the formation of detrimental topologically close-packed (TCP) phases during solidification, such as μ, σ, and Laves phases. , Of particular concern is the presence of the μ phase, a nonstoichiometric intermetallic compound commonly observed in Ni-based, Co-based, and Fe-based high-temperature alloys. , The formation of the μ phase can render the alloy brittle, reducing its plasticity and toughness. This can result in crack initiation and fracture under conditions of high stress and elevated temperature. , Precise control of the presence of the μ phase is necessary during the design and manufacturing of high-temperature alloys to mitigate its adverse effects and achieve optimized material performance.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase

Su,

Wang,

Zou

2023

ACS Omega

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The μ phase is a type of hard and brittle constituent that exists in high-temperature alloys. The formation energy is a crucial thermochemical datum, and the accurate calculation of the formation energy of the μ phase contributes to the material design of high-temperature alloys. Traditional first-principles calculations demand significant computational time and resources. In this study, an innovative machine learning (ML)-based approach to accurately predict the formation energy of the μ phase is proposed. This approach involves the utilization of six algorithms and two model evaluation methods to construct the ML models. Leveraging a comprehensive data set containing 1036 binary configurations of the μ phase, the model trained using a 10-fold cross-validation technique, and the multilayer perceptron (MLP) algorithm achieves a mean absolute error (MAE) of 23.906 meV/atom. To validate its generalization performance, the trained model is further validated on 900 ternary configurations, resulting in an MAE of 32.754 meV/atom. Compared with solely using traditional first-principles calculations, our approach significantly reduces the computational time by at least 52%. Moreover, the ML model exhibits exceptional accuracy in predicting the lattice parameters of the μ phase. The MAE values for the a and c parameters are 0.024 and 0.214 Å, respectively, corresponding to low error rates of only 0.479 and 0.578%. Additionally, the ML model was utilized to accurately predict the formation energy of all of the possible ternary configurations. To enhance accessibility to the formation energy data of the μ phase, a user-friendly graphical user interface (GUI) was developed, ensuring convenient usability for researchers and practitioners.

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