Input Dataset Used to Develop the Analytical Equations in "Physics-Informed Analytical Models for Interpretable and Deployable Hydrogen Storage Prediction in MOFs"

This record is the input dataset used to develop the analytical equations described in the manuscript“Physics-Informed Analytical Models for Interpretable and Deployable Hydrogen Storage Prediction in MOFs” It is a frozen snapshot of the data used for training/validation in the symbolic-regression workflow; it is not a product of the article. The dataset aggregates crystallographic descriptors for MOFs together with GCMC-derived usable hydrogen capacities under 77 K, 100→5 bar, enabling exact reproduction of equation-discovery and benchmarking steps (SISSO, PySR, AI Feynman, gplearn). Scope Systems: Metal–Organic Frameworks (MOFs) Rows: 88,400 MOFs (after quality filters) Targets: Usable gravimetric capacity UCg (wt.%) and usable volumetric capacity UCv (g H₂ L⁻¹) at 77 K, 100→5 bar Descriptors (7): single-crystal density ρc (g cm⁻³), gravimetric surface area Sg (m² g⁻¹), volumetric surface area Sv (m² cm⁻³), pore volume Vp (cm³ g⁻¹), void fraction Fv (–), largest included sphere Di (Å), largest free sphere Df (Å) Contents MOF_SR_Training_Dataset.csv — table with columns: ρc, Sg, Sv, Vp, Fv, Di, Df, UCg, UCv (and any included identifiers) Units (key fields): UCg: wt.% · UCv: g H₂ L⁻¹ · Sg: m² g⁻¹ · Sv: m² cm⁻³ · Vp: cm³ g⁻¹ · ρc: g cm⁻³ · Di, Df: Å Provenance & Use Curated for physics-informed symbolic regression and comparative benchmarking. Suitable for reproducibility, baseline ML, and method comparisons. Please report any issues or clarifications to the contact below. How to Cite Dataset used to develop the analytical equations in “Physics-Informed Analytical Models for Interpretable and Deployable Hydrogen Storage Prediction in MOFs” (manuscript under review). Zenodo, DOI: 10.5281/zenodo.17108560.本数据集为开发稿件《"Physics-Informed Analytical Models for Interpretable and Deployable Hydrogen Storage Prediction in MOFs"》中所述解析方程所用的输入数据集。该数据集是符号回归工作流中训练/验证所用数据的冻结快照，并非该论文的研究产物。本数据集汇总了金属有机骨架（Metal–Organic Frameworks, MOFs）的晶体学描述符，以及77 K、100→5 bar条件下由巨正则蒙特卡洛（Grand Canonical Monte Carlo, GCMC）模拟得到的可用储氢容量，可精准复现方程发现与基准测试流程（包括SISSO、PySR、AI Feynman、gplearn）。 范围 系统：金属有机骨架（Metal–Organic Frameworks, MOFs） 数据行数：经过质量过滤后共88400个MOFs样本 目标变量：77 K、100→5 bar条件下的可用重量容量UCg（wt.%）与可用体积容量UCv（g H₂ L⁻¹） 描述符（共7项）：单晶密度ρc（g cm⁻³）、重量比表面积Sg（m² g⁻¹）、体积比表面积Sv（m² cm⁻³）、孔体积Vp（cm³ g⁻¹）、空隙率Fv（无量纲）、最大包容球直径Di（Å）、最大自由球直径Df（Å） 数据集内容 MOF_SR_Training_Dataset.csv：包含以下列的数据表：ρc、Sg、Sv、Vp、Fv、Di、Df、UCg、UCv（以及相关标识符列） 单位（关键字段）：UCg：wt.%；UCv：g H₂ L⁻¹；Sg：m² g⁻¹；Sv：m² cm⁻³；Vp：cm³ g⁻¹；ρc：g cm⁻³；Di、Df：Å 来源与用途 本数据集专为物理信息驱动的符号回归与对比基准测试整理，可用于研究复现、基准机器学习建模以及方法对比。若存在任何问题或需要说明，请联系对应联系人。 引用方式 本数据集用于开发稿件《"Physics-Informed Analytical Models for Interpretable and Deployable Hydrogen Storage Prediction in MOFs"》（待审稿件）中的解析方程。Zenodo，DOI: 10.5281/zenodo.17108560。