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

Abstract 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.