Most machine-learning synthesizability models overpredict the likelihood of synthesizing hypothetical ternary oxides, relative to thermodynamic stability and reaction-selectivity bounds; only SynthNN tracks the thermodynamic trends closely.
(31) Amariamir, S.; George, J.; Benner, P
1 Pith paper cite this work, alongside 32 external citations. Polarity classification is still indexing.
1
Pith paper citing it
32
external citations · OpenAlex
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
Thermodynamic assessment of machine learning models for solid-state synthesis prediction
Most machine-learning synthesizability models overpredict the likelihood of synthesizing hypothetical ternary oxides, relative to thermodynamic stability and reaction-selectivity bounds; only SynthNN tracks the thermodynamic trends closely.