NaCl-UCl3 Molten Salt Molecular Dynamics Data for Machine Learning Potentials

A 119.4 MB dataset from figshare, last updated March 22, 2026, by author Sudipta Paul. It contains data for developing a machine learning interatomic potential covering the entire compositional space of NaCl-UCl3 molten salt. The dataset is intended to enable accurate, low-cost calculation of thermophysical properties like density, viscosity, and thermal conductivity.

Use Cases

• Training machine learning interatomic potentials based on density functional theory data.
• Analyzing local structural features of NaCl-UCl3 molten salt across its compositional space.
• Calculating thermophysical properties like density and viscosity with ab initio-level accuracy.
• Studying property changes correlated with salt composition for nuclear fuel applications.

Strengths

• Covers the entire compositional space of the binary NaCl-UCl3 melt.
• Potential enables property calculation at a computational cost equivalent to empirical potentials.
• Data is licensed under CC-BY-4.0, allowing for open reuse and modification.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

figshare

Collection Method

Data generated from atomic cluster expansion (ACE) models trained with density functional theory (DFT) data.

Time Range

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Freshness

Last updated 2026-03-22 17:44:58; freshness should be verified.

Geography

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