Machine Learning Accelerated Discovery of Thermal Energy Storage Materials with High Heat Capacity

Existing machine learning potentials for predicting phonon properties of crystals are limited to either small amount of training data or a material-to-material basis, primarily due to the exponential scaling of model parameters with the number of atomic species or elements. This renders high-throughput infeasible when facing large-scale new materials. Unlike previous machine learning models with inherently limited training on small data and global properties, we develop Elemental Spatial Density Neural Network Force Field (Elemental-SDNNFF) with abundant atomic level environments as training data. Benefited from this innovation, we integrate >30 million atomic data to train a single deep neural network without increased expensive ab initio simulations. The effectiveness and precision of the Elemental-SDNNFF approach is demonstrated on a set of >100,000 inorganic crystalline structures spanning 63 elements in the periodic table by prediction of complete phonon properties. We then predicted the heat capacity of >50,000 inorganic crystalline structures with non-zero band gap screened from the Open Quantum Material Database. We gain deep insight into the correlation between heat capacity and structure descriptors such as space group, prototype, lattice volume, atomic weight, etc. Several tens of structures were predicted to possess high heat capacity, and the results were further validated with DFT calculations. We also identified a few special structures that exhibit extremely high heat capacity, even higher than that of the Dulong-Petit limit at room temperature. This study paves the way for accelerating the discovery of novel thermal energy storage materials by combining machine learning with minimal DFT inquiry.