Chahal, R., Gibson, L. D., Roy, S., & Bryantsev, V. S. (Year). Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials. [Journal Name].
This study investigates the feasibility of using machine learning interatomic potentials (MLIPs) to accurately predict the liquid-vapor phase diagram of molten AlCl3, a crucial property for its applications in high-temperature systems.
The researchers developed two types of MLIPs: a kernel-based MLFF and a neural network-based NNIP. They trained these models on ab initio molecular dynamics (AIMD) data generated using the PBE-D3 density functional, which proved most accurate in reproducing experimental AlCl3 properties. Two NNIPs were trained: NNIP-1 using only liquid configurations and NNIP-2 incorporating both liquid and low-density cluster configurations. The trained MLIPs were then employed in molecular dynamics simulations to predict liquid densities, vapor-liquid coexistence curves, critical points, surface tension, and viscosity.
This study demonstrates the effectiveness of MLIPs, particularly NNIPs trained on diverse configurations, in accurately predicting the thermodynamic properties of molten salts like AlCl3. This approach offers a computationally efficient alternative to expensive AIMD simulations for studying complex fluids across a wide range of thermodynamic conditions.
This research has significant implications for the development of safer and more efficient high-temperature applications involving molten salts. The ability to accurately predict vapor pressures and other thermodynamic properties using MLIPs can aid in the design and optimization of molten salt reactors, concentrated solar power plants, and other high-temperature systems.
While this study focused on pure AlCl3, future research should explore the applicability of this approach to more complex, multi-component molten salt systems relevant to real-world applications. Additionally, incorporating long-range electrostatic interactions into MLIP architectures could further improve their accuracy and transferability across different molten salt compositions.
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by Rajni Chahal... at arxiv.org 10-24-2024
https://arxiv.org/pdf/2410.18009.pdfDeeper Inquiries