A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data. Issue 1 (31st December 2018)

• Record Type: Journal Article
• Authors: Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu
• Is Part Of: Science and technology of advanced materials. Volume 19:Issue 1(2018)
• Page Start: 231
• Page End: 242
• Publication Date: 2018-12-31
• Subjects: Materials informatics -- material descriptors -- machine learning -- inorganic solids -- crystalline solids -- density functional theory; 60 New topics/Others -- 401 1st principle calculations -- 404 Materials informatics / Genomics; Materials -- Technological innovations -- Periodicals; 620.112
• DOI: 10.1080/14686996.2018.1439253
• Languages: English
• ISSNs: 1468-6996

Abstract

Abstract: Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density ( d ), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction. Abstract :