Photoemission Spectroscopy of Organic Molecules Using Plane-Wave/Pseudopotential Density Functional Theory and Machine Learning: A Comprehensive and Predictive Computational Protocol for Isolated Molecules, Molecular Aggregates and Organic Thin Films

This dataset is divided in three parts, all connected to the publication "Photoemission Spectroscopy of Organic Molecules Using Plane-Wave/Pseudopotential Density Functional Theory and Machine Learning: A Comprehensive and Predictive Computational Protocol for Isolated Molecules, Molecular Aggregates and Organic Thin Films", DOI: 10.26434/chemrxiv-2024-h0g2d-v2. The dataset is intended as a comprehensive collection of files that enable the users not only to reproduce the results presented in the publication, but also to freely use all methods to calculate and predict photoemission spectra of a large variety of molecules and organic materials, following the tutorial included in the publication. The three parts are defined as follows:

 A repository of tested pseudopotentials enabling core-hole calculations using a variety of DFT exchange-correlation functionals.

A repository of input files that guide the reader to reproduce ground-state and core-ionized DFT calculations, to calculate binding energies and to use references to assign them absolute values.

A repository of datasets used to train machine learning routines to predict C1s, N1s and O1s binding energies in organic molecules and materials containing C, N, O, H, S, and halogen containing organic molecules and materials.