  Materials informatics has emerged as a promisingly new paradigm for
accelerating materials discovery and design. It exploits the intelligent power
of machine learning methods in massive materials data from experiments or
simulations to seek for new materials, functionality, principles, etc.
Developing specialized facility to generate, collect, manage, learn and mine
large-scale materials data is crucial to materials informatics. We herein
developed an artificial-intelligence-aided data-driven infrastructure named
Jilin Artificial-intelligence aided Materials-design Integrated Package
(JAMIP), which is an open-source Python framework to meet the research
requirements of computational materials informatics. It is integrated by
materials production factory, high-throughput first-principles calculations
engine, automatic tasks submission and monitoring progress, data extraction,
management and storage system, and artificial intelligence machine learning
based data mining functions. We have integrated specific features such as
inorganic crystal structure prototype database to facilitate high-throughput
calculations and essential modules associated with machine learning studies of
functional materials. We demonstrated how our developed code is useful in
exploring materials informatics of optoelectronic semiconductors by taking
halide perovskites as typical case. By obeying the principles of automation,
extensibility, reliability and intelligence, the JAMIP code is a promisingly
powerful tool contributing to the fast-growing field of computational materials
informatics.
