Adaptive Learning Framework in Prediction and Validation of Gibbs Free Energy for Inorganic Crystalline Solids

Gibbs free energy is a fundamental
physical property for understanding
the stability and synthesizability of materials under various thermodynamic
conditions, but its accessibility and availability are still limited.
In this study, we used 9880 phonon databases to construct a machine
learning model to predict approximately 40 000 Inorganic Crystalline
Solid Database (ICSD) materials, whose free energy information has
not been fully explored. To improve the prediction accuracy, a sampling
strategy was implemented by including structures with low accuracy
metrics, leading to R2 and mean absolute
error values of 0.99 and 18.7 kJ/mol, respectively, in the testing
set. Uncertainty analysis was followed for unexplored ICSD materials
by obtaining the standard deviation in predictions from 10 surrogate
models with different samplings in the training set. Based on this,
an optimization process was conducted: density functional calculations
were performed for 50 structures with high uncertainty and the training
database was updated; this loop was repeated 15 times. This demonstrates
the reduction and saturation in the uncertainty, confirming that the
constructed model can provide a comprehensive map of the Gibbs free
energy for inorganic solid materials. This can accelerate the material
screening process by providing information on thermodynamic stability.