Machine-learning models of matter beyond interatomic potentials

EurekAlert  January 7, 2021
The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory and underpins the computation and interpretation of experimentally observable material properties such as optical absorption and electrical conductivity. An international team of researchers (Switzerland, UK) studied the configurations of silicon spanning a broad set of thermodynamic conditions, ranging from bulk structures to clusters and from semiconducting to metallic behavior and compared different approaches to represent the DOS, and the accuracy of predicting quantities such as the Fermi level. They found that the performance of the model depends on the resolution chosen to smooth the DOS and the tradeoff between the systematic error associated with the smoothing and the error in the machine learning model for a specific structure. They showed the usefulness of this approach by computing the density of states of a large amorphous silicon sample. The atom-centered decomposition of the DOS that is obtained through their model can be used to extract physical insights into the connections between structural and electronic features…read more. TECHNICAL ARTICLE

Electronic densities of states (DOS) at various stages of the compression run. Credit: Michele Ceriotti

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