JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics.

Journal: Science bulletin
PMID: 36654167

Abstract

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 new materials, functionality, and principles, etc. Developing specialized facilities 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 an 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.

Authors

  • Xin-Gang Zhao
    State Key Laboratory of Integrated Optoelectronics, College of Materials Science and Engineering, Jilin University, Changchun 130012, China.
  • Kun Zhou
    School of Mathematics Science, Peking University, Beijing, China.
  • Bangyu Xing
    State Key Laboratory of Integrated Optoelectronics, College of Materials Science and Engineering, Jilin University, Changchun 130012, China.
  • Ruoting Zhao
    State Key Laboratory of Integrated Optoelectronics, College of Materials Science and Engineering, Jilin University, Changchun 130012, China.
  • Shulin Luo
    State Key Laboratory of Integrated Optoelectronics, College of Materials Science and Engineering, Jilin University, Changchun 130012, China.
  • Tianshu Li
    School of Integrated Circuits, Shandong University, 1500 Shunhua Road, Jinan, 250101, Shandong, China.
  • Yuanhui Sun
    State Key Laboratory of Integrated Optoelectronics, College of Materials Science and Engineering, Jilin University, Changchun 130012, China.
  • Guangren Na
    State Key Laboratory of Integrated Optoelectronics, College of Materials Science and Engineering, Jilin University, Changchun 130012, China.
  • Jiahao Xie
    State Key Laboratory of Integrated Optoelectronics, College of Materials Science and Engineering, Jilin University, Changchun 130012, China.
  • Xiaoyu Yang
    Beijing Jishuitan Hospital, Beijing, China.
  • Xinjiang Wang
    State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China.
  • Xiaoyu Wang
    Department of Statistics Florida State University Tallahassee, FL, USA.
  • Xin He
    Department of Nephrology, The Affiliated Hospital of Guizhou Medical, Guizhou, China.
  • Jian Lv
    Department of Ophthalmology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
  • Yuhao Fu
    State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China. Electronic address: yuhao_fu@jlu.edu.cn.
  • Lijun Zhang
    Department of Paediatric Orthopaedics, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province, China.

Keywords

External Resources

Popular Topics
Recent Journals