Data repository for "Mechanistic insight on water dissociation on pristine low-index TiO$_2$ surfaces from machine learning molecular dynamics simulations"
arXiv preprint: https://arxiv.org/abs/2303.07433
Contains machine learning potentials trained on optb88vdw (./optb88vdw/), and the Delta-learning potentials (./delta-pbe/, ./delta-scan-1200Ry/, ./delta-scan-350Ry/). The Delta-learning potentials need to be used together with the optb88vdw MLP.
The DFT input files for CP2K.
Source data for generating Fig.1. Contains the free energy surfaces, water density profiles, and water orientations.
Source data for generating the kPCA maps in Fig.2.
Source data for generating the kPCA maps in Fig.3a,b, and the transition matrix in Fig.3d.
The example input files for running metadynamics simulations using LAMMPS and PLUMED.
The training data for the optb88vdw MLPs, in N2P2 format. Units are in hartree/Bohr.
The training data for the Delta-learning PBE MLPs, in N2P2 format. Units are in hartree/Bohr.
The training data for the Delta-learning SCAN MLPs, in N2P2 format. Units are in hartree/Bohr.
Python script and notebook to generate features for hydrogen environments, perform classification and kernal PCA.