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msr-acc-tae25-regression

	---
	license: cdla-permissive-2.0
	tags:
	- chemistry
	- smiles
	- cheminformatics
	pretty_name: MSR-ACC TAE25 Regression
	size_categories:
	- 10K<n<100K
	---

	# MSR-ACC TAE25 Regression Dataset

	This dataset is a processed version of the Microsoft Research Accurate Chemistry Collection (MSR-ACC) TAE25 dataset, containing thermochemical data for molecules with computed Total Atomization Energies (TAE) using various quantum chemical methods.

	[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15783826.svg)](https://doi.org/10.5281/zenodo.15783826)

	## Dataset Description

	The original MSR-ACC TAE25 dataset contains QCSchema JSON files with 3D molecular geometries and computed thermochemical properties.

	This processed version provides:
	- SMILES representations generated from 3D coordinates and graph connectivity
	- Train/validation/test splits for machine learning applications
	- Train set: Contains molecular structures from the original training partition (except test samples)
	- Validation set: Contains molecular structures from the original validation partition
	- Test set: 1,510 molecules sampled from the training set (1,485 random samples + 25 complete records)

	## Data Processing Pipeline

	### 1. SMILES Generation

	SMILES strings were generated from 3D molecular coordinates using the following process:

	1. QCSchema Parsing: Loaded QCSchema JSON files using `qcelemental` for validation
	2. Bonds (Graph Connectivity): Used pre-computed connectivity from `graph:all` fields in QCSchema extras
	3. SMILES Generation: Generated canonical, isomeric SMILES using RDKit with `MolToSmiles(canonical=True, isomericSmiles=True)`

	### 2. Dataset Splitting

	The test set was created by sampling from the original training partition to ensure proper evaluation:

	1. Sampling:
	- Selected 25 molecules with complete (non-NaN) target values across all thermochemical properties
	- Randomly sampled 1,485 additional molecules from remaining training data

	2. Data Cleaning:
	- Maintained original train/validation split from source dataset

	## Data Format

	Each Parquet file contains the following columns:

	- `filename`: QCSchema JSON filename from the original dataset
	- `name`: Molecular name from QCSchema
	- `smiles`: Canonical, isomeric SMILES string
	- `symbols`: Atomic symbols
	- `atomic_numbers`: Atomic numbers
	- `geometry`: 3D coordinates in Angstroms
	- `graph:all`: Molecular connectivity information
	- `tae@*`: Total Atomization Energy computed with various DFT methods and basis sets
	- `singlet-triplet-gap-*`: Electronic excitation energies

	## Target Properties

	The dataset includes Total Atomization Energies computed using:

	- DFT Methods: B3LYP, B97M-V, M06-2X, r2SCAN, revDSD-PBEP86
	- Basis Sets: def2-SVP, def2-TZVP, def2-QZVP, ma-def2-*
	- Composite Methods: B97-3C, PBEh-3C, r2SCAN-3C
	- Semi-empirical: GFN1-xTB, GFN2-xTB
	- High-level: CCSD(T)/6-31G*, W1-F12

	## Usage Notes

	- Missing Values: Many molecules have NaN values for specific methods (sparse coverage)
	- SMILES Quality: Generated from optimized 3D geometries and graph connectivity
	- Test Set: Contains both complete and incomplete records to enable various evaluation strategies

	## Citation

	If you use this processed dataset, please cite both the original MSR-ACC publication and the Zenodo repository:

	```bibtex
	@misc{ehlert2025accuratechemistrycollectioncoupled,
	title={Accurate Chemistry Collection: Coupled cluster atomization energies for broad chemical space},
	author={Sebastian Ehlert and Jan Hermann and Thijs Vogels and Victor Garcia Satorras and Stephanie Lanius and Marwin Segler and Derk P. Kooi and Kenji Takeda and Chin-Wei Huang and Giulia Luise and Rianne van den Berg and Paola Gori-Giorgi and Amir Karton},
	year={2025},
	eprint={2506.14492},
	archivePrefix={arXiv},
	primaryClass={physics.chem-ph},
	url={https://arxiv.org/abs/2506.14492},
	}
	```

	```bibtex
	@dataset{ehlert_2025_15783826,
	author = {Ehlert, Sebastian and
	Hermann, Jan and
	Vogels, Thijs and
	Garcia Satorras, Victor and
	Lanius, Stephanie and
	Segler, Marwin and
	Kooi, Derk P. and
	Takeda, Kenji and
	Huang, Chin-Wei and
	Luise, Giulia and
	van den Berg, Rianne and
	Gori-Giorgi, Paola and
	Karton, Amir},
	title = {MSR-ACC/TAE25: 77k coupled cluster atomization
	energies for broad chemical space
	},
	month = jul,
	year = 2025,
	publisher = {Zenodo},
	doi = {10.5281/zenodo.15783826},
	url = {https://doi.org/10.5281/zenodo.15783826},
	}
	```

	## License

	This dataset is released under the Community Data License Agreement - Permissive - Version 2.0 (CDLA-Permissive-2.0), consistent with the original MSR-ACC dataset licensing.