5 papers
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
Ahmed Almeldein, Mohammed Alnaggar, Rick Archibald +47
The AI for Nuclear Energy workshop at Oak Ridge National Laboratory evaluated the potential of Large Language Models (LLMs) to accelerate fusion and fission research. Fourteen inte…
Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials
Rajni Chahal, Luke D Gibson, Santanu Roy +1
Molten salts are promising candidates in numerous clean energy applications, where challenges in experimental methods limit knowledge of their safety-critical temperature-propertie…
Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning
Julian Barra, Shayan Shahbazi, Anthony Birri +6
Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mi…
Uncertainty and Exploration of Deep Learning-based Atomistic Models for Screening Molten Salt Properties and Compositions
Stephen T. Lam, Shubhojit Banerjee, Rajni Chahal
Due to extreme chemical, thermal, and radiation environments, existing molten salt property databases lack the necessary experimental thermal properties of reactor-relevant salt co…
Deep Learning Interatomic Potential Connects Molecular Structural Ordering to Macroscale Properties of Polyacrylonitrile (PAN) Polymer
Rajni Chahal, Michael D. Toomey, Logan T. Kearney +4
Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental un…