Work

Inverse mapping of properties to composition through generative modeling for designing molten salts

Year: 2025

Type: article

Abstract: Generative modeling (GM) has been increasingly used for the inverse design and optimization of materials, yet its application to molten salt mixtures remains unexplored despite how a successful approach to the inverse design of molten salts would contribute to efficiently exploiting their customizability and unlocking their advantages in applications, such as energy production and energy storage. This work presents a workflow for the inverse design of molten salts with targeted density values, addressing the challenge of representing these complex mixtures in GM. A dataset of critically evaluated molten salt densities is used to train a variational autoencoder coupled with a predictive deep neural network, which then can be used to generate new molten salt compositions with desired density values. The effectiveness of the approach is demonstrated by designing mixtures with distinct densities and validating the predicted values using ab initio molecular dynamics simulations.

Source: npj Computational Materials

DOI: 10.1038/s41524-025-01638-x

Authors: Julian Barra, Rajni Chahal, Shubhojit Banerjee, Massimiliano Lupo Pasini, Stephan Irle, Stephen Lam

Institutions: University of Massachusetts Lowell, Oak Ridge National Laboratory

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,490