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README.md

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 This repository contains the dataset used in the paper: **"Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing"** [[link](https://arxiv.org/abs/2601.03774)].
 
+### Computational Details
+All data in this repository were generated using Density Functional Theory (DFT) calculations to ensure the physical accuracy for machine learning training.
+
+* Functional: The ωB97X-D3 range-separated hybrid functional was employed, which includes empirical dispersion corrections to accurately capture long-range van der Waals interactions.
+
+* Basis Set: The def2-SVP basis set was used for all atomic species, providing a robust balance between computational efficiency and electronic structure accuracy.
+
+* System Scale: The dataset covers a wide range of molecular sizes, featuring atomic systems with up to 1,200 atoms, making it uniquely suited for developing scalable models for macromolecular systems.
+
 ### Dataset Components
 The dataset consists of three primary compressed archives, each catering to different aspects of macromolecular force field modeling: