Data for the master's thesis "Machine learning interatomic potentials for ordered mesoporous yttrium silicates" by Daniel Kevin Frank. This dataset contains the files mentioned in the thesis, the three domain-specific Moment Tensor Potentials (MTPs) with their training sets, and instructions to create the Python environments.

The files mentioned in the thesis, including Jupyter Notebooks and LAMMPS in.file(s), can be found in the directories "3 Methodology" and "Appendices", where they are sorted into subdirectories named according to the sections in which the files are referenced. These files are described in greater detail in the master's thesis.

The directory "MTPs and CFGs" is divided into three subdirectories corresponding to the three domain-specific MTPs, namely the "Energy Minimization", "Melt-Quench", and "Hydroxylation" potentials. For each MTP, the VASP OUTCARs of the training set structures are included, which are also given in the MLIP-3 (.cfg) format. Furthermore, for each MTP, the potential (.almtp) file as well as the training script (.sh) and last training output (.out) are present.

The directory "Python Envs" features instructions to create the three required Python environments, namely "DiffPy", "OVITO", and "pyzeo". It should be noted that official installation instructions can change over time.

Note: The hydroxylation potential is a neighborhood (nbh) MTP that has been trained using non-periodic spherical neighborhoods. Including these structures in a training set removes the ability to fit quantum mechanical stresses.