Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning.

ABSTRACT: Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich-Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi-empirical relationships (RK), data fr

SUBMITTER: Barra J 

PROVIDER: S-EPMC12677718 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature