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Page 10 of 20 Li et al. J Mater Inf 2024;4:27 https://dx.doi.org/10.20517/jmi.2024.44

2
Figure 3. The mean RMSE and R values of (A) Model-III and (B) Model-IV constructed by DTR, RFR, SVR, GBR, KNR, and ANNR
algorithms; the comparison of the predicted values by (C) Model-III and (D) Model-IV models and calculated values by CALPHAD.
RMSE: Root mean square error; DTR: decision tree regression; RFR: random forest regression; SVR: support vector regression; GBR:
gradient boosting regression; KNR: k-nearest neighbor regression; ANNR: artificial neural network regression; CALPHAD: calculation of
phase diagrams.

requirements. It is very challenging to control the predicted performance of the proposed design scheme
within a defined range while meeting the structure requirements.

Using the targeted performance criteria (T of 600 °C, UTS of 400 MPa, and TE of 20%) as inputs, the
test
integrated design model was run to design novel RAFM steels meeting both structure and performance
requirements. Table 1 lists two representative compositional and processing schemes, with the
corresponding CALPHAD results presented in Figure 4. Table 2 summarizes the microstructural attributes
of the aforementioned two design schemes calculated by the ML and CALPHAD methods. For 1# and 2#
steels, the ML prediction results show no δ-ferrite or large-size coarsening phases (i.e., Laves and Z-phase),
which is well consistent with the CALPHAD calculations. The ML-predicted V and V M23C6 of 1# steel are
MX
0.52% and 1.41%, respectively, which are very close to the CALPHAD-calculated values of 0.60% and 1.30%.