Automated Computation of Materials Properties

Abstract

Materials informatics offers a promising pathway towards rational materials design, replacing the current trial-and-error approach and accelerating the development of new functional materials. Through the use of sophisticated data analysis techniques, underlying property trends can be identified, facilitating the formulation of new design rules. Such methods require large sets of consistently generated, programmatically accessible materials data. Computational materials design frameworks using standardized parameter sets are the ideal tools for producing such data. This work reviews the state-of-the-art in computational materials design, with a focus on these automated ab-initio frameworks. Features such as structural prototyping and automated error correction that enable rapid generation of large datasets are discussed, and the way in which integrated workflows can simplify the calculation of complex properties, such as thermal conductivity and mechanical stability, is demonstrated. The organization of large datasets composed of ab-initio calculations, and the tools that render them programmatically accessible for use in statistical learning applications, are also described. Finally, recent advances in leveraging existing data to predict novel functional materials, such as entropy stabilized ceramics, bulk metallic glasses, thermoelectrics, superalloys, and magnets, are surveyed. * stefano@duke.edu 1 S. Curtarolo, G. L. W. Hart, M. Buongiorno Nardelli, N. Mingo, S. Sanvito, and O. Levy, The high-throughput highway to computational materials design, Nat. Mater. 12, 191-201 (2013).

Optimization of uniformity in plasma ashing process using genetic programming

“…the optimization of materials and process parameters (see e.g. [3][4][5][6]). In the field of fusion plasma, several studies applying ML to a plasma ion source were reported [7][8][9].…”

Rapid generation of optimal generalized Monkhorst-Pack grids

“…In the years since our previous work was published there has been increasing interest in the generation and use of generalized k-point grids [6,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Of particular note, Hart and co-workers have recently developed algorithms and released an open-source software package (GRkgridgen) for generating generalized k-point grids on the fly [27,28].…”