Machine Learning Potentials and DFT Dataset for Tin-Oxo EUV Photoresists

This repository provides the dataset used in our study: “Competing Sn–O and Sn–C Bond Cleavage Pathways Control Crosslinking in Tin-Oxo EUV Photoresists” The dataset is designed to enable atomistic modeling of photochemical reactions and crosslinking processes in tin–oxo cluster photoresists, with particular emphasis on the competition between Sn–C and Sn–O bond cleavage pathways. Dataset (dataset.tgz): The dataset is provided as a compressed .tgz archive and contains multiple .extxyz files. Eight tin–oxo photoresist systemsEach system is stored as an individual .extxyz file. QM-augmented dataset (QM-AIMD subset)Stored as a separate .extxyz file, consisting of configurations derived from AIMD simulations of small organic molecules to improve model transferability Each frame includes: species: atomic element types pos: atomic coordinates (Å) forces: atomic forces (eV/Å) energy: total energy (eV) Lattice: simulation cell vectors (Å) stress: virial stress tensor (eV/ų) VASP files (VASP-AIMD.tgz): Input file example utilized for AIMD simulation. Machine Learning Potential (mace-pr-model_stagetwo.model): An E(3)-equivariant machine learning potential (MLP) was trained on this dataset. MACE files (mace-train.sh): Input file example utilized for training using MACE.