Exploring Information Density in Crystalline and Amorphous Configurations using Deep Neural Networks

Predicting the properties of amorphous systems is one of the grand challenges for computational material science. Deep neural network potentials (DNP) promise to recreate the chemical fidelity of DFT while scaling to much larger systems, enabling more realistic simulations of amorphous materials. DNPs also provide a tool to analyze large quantities of DFT data by selective training and evaluation. We trained a DNP on the crystalline polymorphs for SiO2 from the Materials Project. This DNP successively predicted the total energies of several DFT computed quasi-amorphous configurations suggesting that the local atomic environments encoded in amorphous compounds are also present in the potential energy space covered by polymorphism. More importantly, a DNP trained on just the amorphous configurations was able to predict the energies of polymorphs, including the ground state configuration over 100 meV/atom below the lowest included amorphous configuration. This suggests that DNPs trained on quasi-amorphous configurations may be an effective means of identifying ground state configurations as well as polymorphism in never before explored systems.