Database of small molecule X-ray absorption spectra, featurized structures, and neural network ensembles

Companion data for arXiv preprint Uncertainty-aware predictions of molecular X-ray absorption spectra using neural network ensembles (https://arxiv.org/abs/2210.00336), by Animesh Ghose, Mikhail Segal, Fanchen Meng, Zhu Liang, Mark S. Hybertsen, Xiaohui Qu, Eli Stavitski, Shinjae Yoo, Deyu Lu & Matthew R. Carbone.

Included

*-XANES-*.tar.bz2: raw input/output files for all molecular simulations used in the work. These inputs and outputs correspond to the structural data in the QM9 dataset.

ml_ready.tar.bz2: machine learning-ready data (featurized spectra). Used as input to the neural network ensembles.

XANES-220712-ACSF-*.tar.bz2: neural network ensembles used in this work.

Notes

The FEFF9 code [J. J. Rehr, J. J. Kas, F. D. Vila, M. P. Prange, and K. Jorissen, Phys. Chem. Chem. Phys. 12, 5503 (2010)] was used to generate all X-ray absorption near-edge structure (XANES) spectra.

All molecular structures were sourced from the QM9 database [R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, Sci. Data 1, 1 (2014)].

Funding

This research is based upon work supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. This research also used theory and computational resources of the Center for Functional Nanomaterials, which is a U.S. Department of Energy Office of Science User Facility, and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704.