FF7: A Code Package for High-throughput Calculations and Constructing Materials Database

TL;DR

The paper introduces FF7, a user-friendly code package designed for high-throughput material calculations and database construction, facilitating data reuse and machine learning applications in materials science.

Contribution

FF7 provides an interactive command-line tool for customized high-throughput calculations, database building, and integrated machine learning modules, addressing gaps in existing tools.

Findings

Demonstrated thermodynamic stability calculations using FF7.

Built a machine learning model for superconducting critical temperature prediction.

Showcased the ease of database construction and data analysis with FF7.

Abstract

Decades accumulation of theory simulations lead to boom in material database, which combined with machine learning methods has been a valuable driver for the data-intensive material discovery, i.e., the fourth research paradigm. However, construction of segmented databases and data reuse in generic databases with uniform parameters still lack easy-to-use code tools. We herein develop a code package named FF7 (Fast Funnel with 7 modules) to provide command-line based interactive interface for performing customized high-throughput calculations and building your own handy databases. Data correlation studies and material property prediction can progress by built-in installation-free artificial neural network module and various post processing functions are also supported by auxiliary module. This paper shows the usage of FF7 code package and demonstrates its usefulness by example of database driven thermodynamic stability high-throughput calculation and machine learning model for predicting the superconducting critical temperature of clathrate hydrides.