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材料基因工程加速新材料设计与研发

孙志梅 王冠杰 张烜广 周健

孙志梅, 王冠杰, 张烜广, 等 . 材料基因工程加速新材料设计与研发[J]. 北京航空航天大学学报, 2022, 48(9): 1575-1588. doi: 10.13700/j.bh.1001-5965.2022.0318
引用本文: 孙志梅, 王冠杰, 张烜广, 等 . 材料基因工程加速新材料设计与研发[J]. 北京航空航天大学学报, 2022, 48(9): 1575-1588. doi: 10.13700/j.bh.1001-5965.2022.0318
SUN Zhimei, WANG Guanjie, ZHANG Xuanguang, et al. Novel material design and development accelerated by materials genome engineering[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(9): 1575-1588. doi: 10.13700/j.bh.1001-5965.2022.0318(in Chinese)
Citation: SUN Zhimei, WANG Guanjie, ZHANG Xuanguang, et al. Novel material design and development accelerated by materials genome engineering[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(9): 1575-1588. doi: 10.13700/j.bh.1001-5965.2022.0318(in Chinese)

材料基因工程加速新材料设计与研发

doi: 10.13700/j.bh.1001-5965.2022.0318
基金项目: 

国家自然科学基金 51872017

中国博士后科学基金 2022TQ0019

北航高性能计算平台 

详细信息
    通讯作者:

    孙志梅, E-mail: zmsun@buaa.edu.cn

  • 中图分类号: TB39

Novel material design and development accelerated by materials genome engineering

Funds: 

National Natural Science Foundation of China 51872017

China Postdoctoral Science Foundation 2022TQ0019

The High Performance Computing (HPC) Resources at Beihang University 

More Information
  • 摘要:

    在未知材料化学成分和性能关系的情况下,通过传统的“试错-纠错”方法研发具有特定功能的新材料成本高且经常失败。随着人工智能和数据驱动的第四科学范式的发展,材料基因工程(MGE)已经成为材料设计与研发的新模式。综述了材料基因工程中高通量计算、材料数据库和人工智能方法的研究进展。介绍了材料高通量计算常用的框架和方法; 阐述了材料数据库在材料数据类型和数据标准两方面的发展现状和有待解决的难题; 总结了人工智能方法在材料关键基础问题中的应用。从高通量可视化计算方法、材料多类型数据库和可视化机器学习框架三方面重点证述了自主开发的多尺度集成可视化的高通量自动计算和数据管理智能平台ALKEMIE。展望了材料基因工程未来的发展趋势。

     

  • 图 1  科学发展的四个范式[6]

    Figure 1.  Four paradigms of science[6]

    图 2  Pymatgen高通量软件工作原理[7]

    Figure 2.  Principle of Pymatgen high-throughput software[7]

    图 3  Atomate中高通量能带计算流程[7]

    Figure 3.  High-throughput computational workflow of band structure in Atomate[7]

    图 4  二维材料表面和异质结的高通量计算软件MPInterfaces工作流程[12]

    Figure 4.  Workflow for 2D material surfaces and heterojunctions in MPInterfaces software[12]

    图 5  Materials Project Database数据库概况[7]

    Figure 5.  Snapshot of Materials Project Database[7]

    图 6  ALKEMIE-DB数据库中高通量能带和态密度计算结果可视化[14]

    Figure 6.  Visualization of high-throughput band structures and density of states calculations in ALKEMIE-DB databases[14]

    图 7  材料数据共享标准OPTIMADE概况[31]

    Figure 7.  Overview of materials data sharing standard of OPTIMADE[31]

    图 8  多尺度集成可视化的高通量自动计算和数据管理智能平台ALKEMIE计算模块概况

    Figure 8.  Overview of platform with multi-scale integration of visualized automatic high-throughput calculation and intelligent data management ALKEMIE

    图 9  ALKEMIE-DB材料结构数据库[14]

    Figure 9.  Material structure database of ALKEMIE-DB[14]

    图 10  ALKEMIE-PM机器学习框架

    Figure 10.  Framework of ALKEMIE-PM machine learning

    表  1  材料高通计算软件和框架发展现状

    Table  1.   Software and frameworks of material high-throughput calculation

    名称 说明 网址
    ALKEMIE 高通量计算框架和软件 https://alkemine.org/
    Pymatgen 高通量计算框架 http://pymatgen.org/
    FireWorks 高通量计算框架 https://materialsproject.github.io/fireworks/
    Atomate 高通量计算框架和软件 https://atomate.org/
    AFLOW-π 高通量计算软件 http://aflowlib.org/scr/aflowpi/
    ASE 高通量计算框架和软件 http://wiki.fysik.dtu.dk/ase/
    AiiDA 高通量计算框架和软件 http://aiida.net/
    Abipy 高通量计算软件 https://github.com/abinit/abipy
    MPInterfaces 高通量计算框架 http://henniggroup.github.io/MPInterfaces/
    Imeall 高通量计算软件 https://github.com/Montmorency/imeall
    MedeA 高通量计算软件 https://www.materialsdesign.com/
    IprPy 高通量计算框架 https://www.ctcms.nist.gov/potentials/iprPy/
    Pylada 高通量计算框架 http://pylada.github.io/pylada
    MIP 高通量计算软件 http://www.mip3d.org/
    MatCloud 高通量计算软件 http://matcloud.cnic.cn/
    JAMIP 高通量计算框架 http://jamip-code.com
    MatAi 高通量计算框架 https://www.mat.ai/
    中国材料基因工程高通量计算平台 高通量计算框架 http://mathtc.nscc-tj.cn/
    下载: 导出CSV

    表  2  材料多类型数据库的发展

    Table  2.   Development of multi-type databases of materials

    名称 说明 网址
    ALKEMIE-DB 通用材料数据库 https://alkemine.net/
    ICSD 晶体结构数据库 http://www2.fiz-karlsruhe.de/icsd_home.html
    COD 晶体结构数据库 http://crystallography.net
    AFLOWLIB 材料计算数据库 http://aflowlib.org/
    MP Database 材料计算数据库 http://materialsproject.org/
    NOMAD 材料计算数据库 https://repository.nomad-coe.eu/
    OQDM 材料计算数据库 http://oqmd.org/
    CMR 材料计算数据库 https://cmr.fysik.dtu.dk/
    OMDB 电子结构数据库 https://omdb.mathub.io/
    JARVIS-DFT 材料赝势数据库 https://ctcms.nist.gov/-knc6/JVASP.html
    C2DB 二维材料数据库 https://cmr.fysik.dtu.dk/c2db/c2db.html
    AMSD 矿物材料数据库 http://rruff.geo.arizona.edu/AMS/amcsd.php
    ASM 合金材料数据库 https://mio.asminternational.org/ac
    CatApp 催化材料数据库 https://suncat.stanford.edu/theory/it-facilities
    HSMD 储氢材料数据库 https://hydrogenmaterialssearch.govtools.us
    Thermocalc 热力学数据库 https://thermocalc.com/products/databases/
    Citrination 通用材料数据库 https://citrination.com
    MatNavi 通用材料数据库 https://mits.nims.go.jp/en
    NIST 通用材料数据库 https://www.nist.gov/programs-projects
    Materials Web 通用材料数据库 https://materialsweb.org/twodmaterials
    Materials Cloud 通用材料数据库 https://materialscloud.org/discover
    MSDSN 通用材料数据库 http://www.materdata.cn/
    Atomly 通用材料数据库 https://atomly.net/
    MCDC 腐蚀防护数据库 https://www.corrdata.org.cn/
    MGED 材料计算数据库 https://www.mgedata.cn/
    下载: 导出CSV
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  • 收稿日期:  2022-05-06
  • 录用日期:  2022-05-18
  • 网络出版日期:  2022-06-01

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