Unstable Crystallographic & Molecular Structures for Machine Learning of System Energies

Extended QM9 (E-QM9) includes diverse sizes (i.e. number of atoms) and compositions of OoE molecules, through extending a subset of QM9 with OoE versions of 10k of its molecules.

Periodic crystals (PC) allows learning regular bonding patterns that arise in periodic structures by repeating the base crystal lattice. We use the Face-Centred Cubic (fcc) Bravais lattice for aluminium (Al) and copper (Cu) crystals.

Crystal Growth (CG) contains growing crystals of increasing size and complexity. Starting from a basic fcc crystal seed of 14 atoms, new systems are generated by iteratively placing atoms at a random location on the surface of the growing crystal following its lattice pattern, with sizes ranging from 15 to 114 atoms. We use 20 random seeds for each atom type, thus creating 40 varied Al and Cu crystal growths and 4,000 stable systems. As a result, for a given crystal size and composition (atom type), there are 20 samples with differently located atoms. CG enables experi- menting with large scale atomic interactions in non-regular sys- tems, and enables evaluation of an ML method’s ability to learn how each atom contributes to the final potential energy.

In all datasets, OoE systems are obtained by compressing/dilating all interatomic distances (i.e. isometrically) at regular intervals within 90-150% of stable geometry, which we refer to as ‘scaling’. In other words, scaling is applied to the coordinates of all atoms within the system. At each geometry, the ground-truth potential energy is calculated using CP2K7’s DFT.