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.