Machine Learning-Assisted Materials Design and Discovery of Low-Melting-Point Inorganic Oxides for Low-Temperature Cofired Ceramic Applications

The
fabrication of low-temperature cofired ceramics (LTCCs) densified
at a low sintering temperature (<900 °C) is energy-saving
and environmentally friendly. However, finding novel LTCC materials
by the trial-and-error method is time-consuming and costly. The LTCC
materials often have low melting points, so it is feasible to discover
high-performance LTCC materials out of the low-melting-point ceramics.
A two-stage machine learning framework was adopted to establish the
melting-point prediction model for inorganic oxides. Chemical compositions
were used as features in stage 1 modeling; while in stage 2, more
features were integrated according to domain knowledge to optimize
the prediction model. Stage 2 model built by an artificial neural
network algorithm shows the best performances with R2 = 0.7968 and root-mean-square error = 247.4 (K). Three
features, including formation energy per atom (fepa), theoretical
density (d), and number of atoms (na), were extracted
as the decisive characteristics of inorganic oxides. The melting point
demonstrates positive correlations with the absolute value of fepa
and d. The na acts as a “recessive gene”
because its contribution is indirect but necessary. The physical relationships
between features and the melting point were also discussed. Furthermore,
the LTCC inorganic oxides often have melting points lower than 1400
°C statistically. This criterion was verified by the reported
LTCC/ultra-LTCC materials. The melting points of materials in the
prediction set consisting of ∼3600 inorganic oxides were calculated
by the ML model, and thus, the underlying LTCC materials could be
screened out efficiently.