  Thermodynamics is fundamental for understanding and synthesizing
multi-component materials, while efficient and accurate prediction of it still
remain urgent and challenging. As a demonstration of the "Divide and conquer"
strategy decomposing a phase diagram into different learnable features,
quantitative prediction of melting temperature of binary alloys is made by
constructing the machine learning (ML) model "MeltNet" in the present work. The
influences of model hyperparameters on the prediction accuracy is
systematically studied, and the optimal hyperparameters are obtained by
Bayesian optimization. A comprehensive error analysis is made on various
aspects including training duration, chemistry and input features. It is found
that except a few discrepancies mainly caused by less satisfactory treatment of
metalloid/semimetal elements and large melting point difference with poor
liquid mixing ability between constituent elements, MeltNet achieves overall
success in prediction, especially capturing subtle composition-dependent
features in the unseen chemical systems for the first time. The reliability,
robustness and accuracy of MeltNet is further largely boosted by introducing
the ensemble method with uncertainty quantification. Based on the
state-of-the-art underlying techniques, MeltNet achieves a prediction mean
average error (MAE) as low as about 120 K, at a minimal computational cost. We
believe the present work has a general value for significant acceleration of
predicting thermodynamics of complicated multi-component systems.
