2021/05/26 by P. Villars, Villars, Pierre, Evgeny Blokhin +3
Engineering · Materials Science · #Advanced Materials Characterization Techniques #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography
paper · pdf · doi:10.48550/arxiv.2105.12784
openalex publication_date 2021/05/26 · openalex created_date 2021/06/07 · openalex updated_date 2026/07/28
The fundamental relationship of the atomic structure (represented by its atomic property parameters, APPs) and its physical properties of a specific inorganic substance can be realized in the bottom-up data-centric and the top-down knowledge physics-centric ways. Nowadays these two approaches compete and enhance one another qualitatively and quantitatively. We present our own holistic method and implementation, based on the PAULING FILE peer-reviewed inorganic substances database, the world largest materials database containing under one shelter crystallographic structures, phase diagrams and large variety of physical properties of single-phase inorganic substances. In addition we present generated machine-learning data, as well as simulated DFT physics-centered data, which are in close connection and comparison with the PAULING FILE peer-reviewed reference data.