ALKEMIE: An intelligent computational platform for accelerating materials discovery and design

• Published in: Computational materials science Vol. 186; p. 110064
• Main Authors: Wang, Guanjie, Peng, Liyu, Li, Kaiqi, Zhu, Linggang, Zhou, Jian, Miao, Naihua, Sun, Zhimei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2021
• Subjects: Graphical user interface; High-throughput calculations; Machine learning; Materials informatics; Materials informatics; High-throughput calculations; Machine learning; Graphical user interface
• ISSN: 0927-0256; 1879-0801
• DOI: 10.1016/j.commatsci.2020.110064

[Display omitted] Developing new materials with target properties via the traditional trial-and-error ways is cost-inefficient, and sometimes ends up with fruitlessness, therefore, simulation-driven materials design plays an important role in the past decades. Nevertheless, the advent of the era of data-driven material science requires an intelligent computational platform to accelerate the discovery and design for advanced materials. Here, we present an open-source computational platform named as ALKEMIE, acronyms for Artificial Learning and Knowledge Enhanced Materials Informatics Engineering, which enables easy access of data-driven techniques to broad communities. ALKEMIE is incorporated with three key components for the computational design of materials for the forthcoming data-driven sciences: data generation via high-throughput calculations, data management and data mining via machine learning models. Briefly speaking, the high-throughput calculations in ALKEMIE are implemented through the integration of automatic frameworks of model constructions, calculation performances and data analysis. And the used high-level application programming interface for the database makes the data mining through machine learning more applicable in material science. In particular, ALKEMIE is integrated with a module for the generation of machine-learned interatomic potential for large-scale molecular dynamic simulations where the dataset is obtained from high-throughput first-principles calculations. More importantly, ALKEMIE has an elaborately designed user-friendly graphical user-interface which makes the workflow and dataflow more maneuverable and transparent, facilitating its easy-to-use for scientists with broad backgrounds. Finally, the main characters of ALKEMIE are demonstrated using three computational examples.