README.md

metadata

pretty_name: SPT XANES Dataset
license: cc-by-nc-sa-4.0
language:
  - en
task_categories:
  - tabular-classification
  - tabular-regression
size_categories:
  - 10K<n<100K
tags:
  - xanes
  - x-ray-absorption-spectroscopy
  - materials-science
  - chemistry
  - structure-property-prediction
  - scientific-machine-learning
configs:
  - config_name: computed_non_oxygen
    default: true
    data_files:
      - split: full
        path: datasets/computed/metal.csv
  - config_name: computed_oxygen
    data_files:
      - split: full
        path: datasets/computed/oxygen.csv

SPT XANES Dataset

Inferring structural descriptor information from X-ray absorption spectra through adaptive multiscale learning

Suyang Zhong, Boying Huang, Pengwei Xu, Fanjie Xu, Yuhao Zhao,
Jun Cheng, Fujie Tang, Weinan E, Zhong-Qun Tian

Code & experiments · Overview · Download & use · Data index · Citation

⚠️ Usage Restriction

Author-owned data and documentation in this release are licensed under CC BY-NC-SA 4.0, for non-commercial use only. Commercial use requires separate permission from the relevant rights holders. Third-party materials retain their original licences; see Licence and attribution.

📖 Dataset overview

This is the dataset collection for the Spectral Pattern Translator (SPT), which infers electronic, local-geometric and crystallographic descriptors from X-ray absorption near-edge structure (XANES). It contains computed oxide spectra with structure-derived labels, prepared measured spectra with their source information, fixed data partitions and model reference predictions. The release supports the benchmark, spectral-control, application and transfer-learning experiments in the accompanying manuscript.

Collection	Spectra	Purpose
Computed non-oxygen sites	26,870	Seven descriptor tasks; Li, Co, Mn, P, Fe, Ni and Ti absorbers
Computed oxygen sites	27,874	Five descriptor tasks; O absorbers
Computed Li–Co–O application	17	Evaluation on Co sites in ordered LiₓCoO₂ structures
Measured applications	78	Six reference, charging, activation and sodiation cohorts
Computed disordered target collection	399	OS, CN and OCN transfer-learning experiments

The main benchmark contains 54,744 computed spectra and 12 site-specific descriptor tasks. Each processed spectrum contains 256 energy–intensity points. Computed and measured data are identified separately throughout the collection.

Framework overview. SPT combines multiscale spectral processing with relationships between spectral energy points. The example input is a computed Co K-edge spectrum; internal features, connections and descriptor glyphs are schematic. Implementation details and experiment commands are in the code repository.

📦 Data access

Dataset branch	Companion code	Contents
main	SPT main	Benchmark tables, application inputs and sources, Li–Co–O coordinates and site mapping, transfer data, fixed partitions and reference predictions

Model checkpoints and configurations are distributed separately on the main branch of the SPT model repository. This dataset can be downloaded and inspected independently of the weights.

🗂️ Repository contents

Directory or file	Contents
datasets/computed/	Non-oxygen and oxygen computed-spectrum tables
datasets/applications/	Prepared application inputs, source information and descriptor predictions
datasets/controls/	Inputs for the reported spectral controls
datasets/transfer/	Source and target-domain transfer data
data/splits/	Fixed structure-level partitions
reference_predictions/	Per-spectrum model outputs
reference_results/	Benchmark, uncertainty and control summaries
datasets/DATA_INDEX.csv	File roles and locations
ASSET_MANIFEST.json	Asset paths and file sizes
images/	Framework illustration
CITATION.cff, LICENSE	Dataset citation metadata and repository licence

The data index describes individual files. The asset manifest lists numerical assets and their sizes. Reference prediction files contain model outputs, distinct from structure-derived labels and experimental comparison values.

📊 Computed benchmark

Tables and partitions

Site family	Input table	Parent structures	Train	Validation	Test
Non-oxygen	metal.csv	3,201	18,911	3,989	3,970
Oxygen	oxygen.csv	3,123	19,633	4,117	4,124

Counts are spectra before descriptor-specific class eligibility filtering. Within each site family, all absorbing sites from one parent structure share a partition. The assignments are stored in metal.json and oxygen.json, keyed by material_id. Use these assignments and the selected model configuration for the reported experiments.

The Hub configurations computed_non_oxygen and computed_oxygen expose each complete table as a full split for browsing. This is not a training split. Classification eligibility and physical-value-to-class-index mappings are specified in the model configurations.

Data fields

Field	Meaning
Unnamed first column (Unnamed: 0 in pandas)	Saved zero-based row identifier
material_id	Parent-structure identifier used by the partition files
center_atom_id	Absorbing-site identifier within the structure
center_atom_type	Absorbing-element symbol
XAS_Relative_energy	Serialized list of 256 energy coordinates in eV; retain the supplied coordinate convention
XAS_Intensity	Serialized list of 256 corresponding computed intensities
Descriptor columns	Physical labels or values described below

The spectrum columns are numeric lists stored as CSV strings. Parse them with ast.literal_eval, as shown below. Model input normalization is applied by the SPT loader.

Descriptor tasks

Absorbing sites	Classification	Regression
Non-oxygen	OS, CN, OCN, CN2, SGN	NNRS, MOOD
Oxygen	CN, CN2, SGN	NNRS, NNOSM

Descriptor	Definition	Unit
OS	Bond-valence-assigned oxidation state of the absorber	Dimensionless
CN	Number of selected first-shell neighbours	Count
OCN	Number of oxygen atoms among those first-shell neighbours	Count
CN2	Distinct sites reached by two successive neighbour searches, excluding the absorber	Count
SGN (PSGO in files)	Space-group number of the parent crystal	Categorical identifier
NNRS	Standard deviation of absorber–neighbour distances: oxygen neighbours for non-oxygen absorbers, all first-shell neighbours for oxygen absorbers	Å
MOOD	Minimum nonzero O–O distance among first-shell oxygen neighbours	Å
NNOSM	Mean assigned oxidation state of the first-shell neighbours	Dimensionless

The benchmark has eight classification and four regression tasks. The retained NNAS columns and oxygen-site OS column are not benchmark targets. Negative oxidation-state values are valid labels, not missing-value codes. Encoded prediction indices must be decoded with the corresponding model's class map.

🔬 Application spectra and sources

Cohort directory	Type	Spectra	Source
lco_computed_co_sites	Computed	17	Ordered LiₓCoO₂ structures: Ran et al.; structure repository
lco_measured_charging	Measured	11	Lahtinen et al.; processed spectra supplied by the study authors; study record
co_lisa_compounds	Measured	8	LISA XAS database; original XDI spectra and calibration information
ti_nist_standards	Measured	9	NIST BMM standards; original XDI spectra and NIST licence notice
fe_charging	Measured	36	DANOISE study; Zenodo data
ni_fe_activation	Measured	8	Ni–Fe activation study; source data
ni_first_sodiation	Measured	6	Ni sodiation study; Zenodo data

Each cohort includes a prepared.csv, source information in provenance.json, and its reference predictions. Original or supplied source files are stored in raw/, with additional preparation context in source_context/ where available. Ni–Fe activation contains six activation-state spectra and two separate reference compounds. The Fe cohort uses the first 36 binned charging scans.

The computed Li–Co–O cohort includes seven VASP coordinate files and a Co-site mapping table. The table links each of the 17 spectra and its stored label row to a coordinate file and zero-based Co atom index. Atom order is preserved. CN, OCN, CN2 and SGN reproduce the stored labels; NNRS and MOOD agree within numerical precision under the neighbour and periodic-distance conventions in Supplementary Information Section 10.1.

Prepared spectra use long-format CSV:

Column	Meaning
spectrum_id	Spectrum identifier
point_index	Point order, 0–255 within each spectrum
energy_eV	Prepared energy coordinate in eV
intensity	Prepared spectral intensity; model-specific normalization is applied during inference
absorber	Absorbing-element symbol

Cohort-specific metadata include voltage, state of charge, scan number, sample name or activation state. The prepared inputs preserve the energy referencing and resampling used in the reported applications. Application documentation gives the preparation and source details. Application NNRS and MOOD predictions are in ångströms.

🔄 Transfer data and reference results

datasets/transfer/ separates the crystalline computed source data, computed disordered target data and task-specific training subsets. The OS, CN and OCN target tables provide entry points to the same 399-spectrum target collection. The fixed transfer partition contains 285 training-pool spectra and 114 evaluation spectra; the training subsets contain 29, 72 or 143 spectra. Partition and subset definitions are in data/splits/transfer/. These are computed spectra, not experimental measurements.

reference_predictions/ contains per-spectrum outputs for the computed benchmark, transfer experiments and uncertainty analyses. reference_results/ contains numerical summaries. Use the experiment guides and transfer guide to select the corresponding inputs and models.

📥 Download and use

Download the collection

Install the Hugging Face client. The collection is public and can be downloaded without authentication.

python -m pip install -U huggingface_hub
hf download zsysuyang/XAS_SPT --repo-type dataset \
  --revision main --local-dir spt-data

Load a computed table

Install the libraries for the standalone loading examples:

python -m pip install pandas datasets

import ast
import pandas as pd

table = pd.read_csv("spt-data/datasets/computed/metal.csv", index_col=0)
energy = ast.literal_eval(table.iloc[0]["XAS_Relative_energy"])
intensity = ast.literal_eval(table.iloc[0]["XAS_Intensity"])
print(table.shape, len(energy), len(intensity))  # (26870, 13), 256, 256

The two computed subsets can also be loaded with the Hugging Face datasets library:

from datasets import load_dataset

spectra = load_dataset(
    "zsysuyang/XAS_SPT",
    "computed_non_oxygen",
    revision="main",
    split="full",
)

Run SPT inference

Clone and install SPT. Copy datasets/, data/splits/, reference_predictions/ and reference_results/ from this collection into the matching code checkout. Retain the code checkout's root README and complete data/asset_manifest.json. Install the separate checkpoint collection in checkpoints/ before inference.

From the assembled code checkout:

spt verify-assets --all-assets
spt application --cohort ni_fe_activation \
  --tasks OS CN OCN NNRS MOOD \
  --output outputs/ni_fe_activation.csv

The Ni applications use their registered CPU settings. Other cohorts and the computed reference experiments use the environments specified in the application guide and numerical-experiment guide. The model registry maps each experiment to its data and checkpoint.

🎯 Intended use

The collection supports descriptor-learning benchmarks, spectral sensitivity analysis and evaluation on the supplied application cohorts. Computed labels derive from the corresponding structures; measured-data comparison labels and EXAFS values are identified in the cohort source information. Reference predictions are not substitutes for experimental ground truth. Apply the supplied partitions, descriptor eligibility and preprocessing when comparing with the reported results.

⚖️ Licence and attribution

Unless separately indicated, author-owned data and documentation in this release are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). Attribution and ShareAlike conditions apply as specified in the licence. For separate commercial permission, contact Jun Cheng or Fujie Tang.

Third-party spectra, structures and source files retain their original terms and attribution requirements. In particular, retain the NIST licence notice. The repository-level licence does not override these source-specific terms or revoke rights previously granted under Apache-2.0.

📝 Citation

Please cite this dataset collection and the accompanying manuscript. Dataset metadata are provided in CITATION.cff.

@misc{zhong2026sptdata,
  author = {Zhong, Suyang and Huang, Boying and Xu, Pengwei and Xu, Fanjie
            and Zhao, Yuhao and Cheng, Jun and Tang, Fujie and E, Weinan
            and Tian, Zhong-Qun},
  title = {SPT XANES Dataset},
  year = {2026},
  howpublished = {Hugging Face},
  url = {https://huggingface.co/datasets/zsysuyang/XAS_SPT}
}

Suyang Zhong, Boying Huang, Pengwei Xu, Fanjie Xu, Yuhao Zhao, Jun Cheng, Fujie Tang, Weinan E and Zhong-Qun Tian. Inferring structural descriptor information from X-ray absorption spectra through adaptive multiscale learning.

Please also cite the original experimental sources used in your analysis. For data or usage questions, open an SPT issue. Scientific correspondence: Jun Cheng and Fujie Tang.