How Chemical Composition Alone Can Predict Vibrational Free Energies and Entropies of Solids

摘要

Computing vibrational free energies (Fvib) and entropies (Svib) has posed a long-standing challenge to the high-throughput ab initio investigation of finite temperature properties of solids. Here, we use machine-learning techniques to efficiently predict Fvib and Svib of crystalline compounds in the Inorganic Crystal Structure Database. Using descriptors based simply on the chemical formula and using a training set of only 300 compounds, mean absolute errors of less than 0.04 meV/K/atom (15 meV/atom) are achieved for Svib (Fvib), whose values are distributed within a range of 0.9 meV/K/atom (300 meV/atom.) In addition, for training sets containing fewer than 2000 compounds, the chemical formula alone is shown to perform as well as, if not better than, four other more complex descriptors previously used in the literature. The accuracy and simplicity of the approach means that it can be advantageously used for fast screening of chemical reactions at finite temperatures.