Table 1.

Notations for the different scratch (SC) and transfer learning (TL) modeling configurations used in this work.

Notation	Description
Base	Naive model that simply uses the average property value of the training data as the predicted value
SC : ML(EF)	ML model trained from scratch using elemental fractions (EF) as input
SC : ML(PA)	ML model trained from scratch using physical attributes (PA) as input
SC : DL(EF)	DL model trained from scratch using EF as input
SC : DL(PA)	DL model trained from scratch using PA as input
TL : ML(FeatExtr)	ML model trained from the activations extracted from the source model (except for last layer)
TL : DL(FeatExtr)	DL model trained from the activations extracted from the source model (except for last layer)
TL : FineTune	Fine-tuning on the same DL framework using the pre-trained weights of source model
TL : ModFineTune	Fine-tuning on the same DL framework using the pre-trained weights of source model except for the last layer which has randomly initialized weights
TL : freezing	DL model trained from the activations extracted from the last layer of the source model