高通量自动流程集成计算与数据管理智能平台及其在合金设计中的应用

doi:10.11900/0412.1961.2021.00041

金属学报

2022, Vol. 58

Issue (1): 75-88 DOI: 10.11900/0412.1961.2021.00041

研究论文

本期目录 | 过刊浏览

高通量自动流程集成计算与数据管理智能平台及其在合金设计中的应用

王冠杰¹^,², 李开旗¹^,², 彭力宇¹^,², 张壹铭¹^,², 周健¹^,², 孙志梅¹^,²(

)

^1. 北京航空航天大学材料科学与工程学院北京 100191
^2. 北京航空航天大学国际交叉科学研究院集成计算材料工程中心北京 100191

High-Throughput Automatic Integrated Material Calculations and Data Management Intelligent Platform and the Application in Novel Alloys

WANG Guanjie¹^,², LI Kaiqi¹^,², PENG Liyu¹^,², ZHANG Yiming¹^,², ZHOU Jian¹^,², SUN Zhimei¹^,²(

)

^1. School of Materials Science and Engineering, Beihang University, Beijing 100191, China
^2. Center for Integrated Computational Materials Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing 100191, China

引用本文:

王冠杰, 李开旗, 彭力宇, 张壹铭, 周健, 孙志梅. 高通量自动流程集成计算与数据管理智能平台及其在合金设计中的应用[J]. 金属学报, 2022, 58(1): 75-88.
Guanjie WANG, Kaiqi LI, Liyu PENG, Yiming ZHANG, Jian ZHOU, Zhimei SUN. High-Throughput Automatic Integrated Material Calculations and Data Management Intelligent Platform and the Application in Novel Alloys[J]. Acta Metall Sin, 2022, 58(1): 75-88.

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摘要:

材料研发模式经历了经验主导的第一范式、理论模型主导的第二范式和计算模拟主导的第三范式，如今正处于数据驱动的第四范式。为加速新材料的设计与研发，发展基于材料数据库和人工智能算法的高通量自动集成计算和数据挖掘算法变得至关重要。本文介绍了作者团队自主开发的分布式高通量自动流程集成计算和数据管理智能平台ALKEMIE2.0 (Artificial Learning and Knowledge Enhanced Materials Informatics Engineering 2.0)，该平台基于AMDIV设计理念，包含了自动化、模块化、数据库、人工智能和可视化流程等5个适用于数据驱动的材料研发模式核心要素。概括来说，ALKEMIE2.0以模块化的方式集成了多个不同尺度的计算模拟软件；其高通量自动纠错流程可实现从建模、运行到数据分析，全程自动无人工干预；支持单用户不低于10⁴量级的并发高通量自动计算模拟。进一步而言，ALKEMIE2.0具有强大的可移植性和可扩展性，目前已部署在国家超算天津中心，基于多类型材料数据库结合超算强大的计算能力使得人工智能算法在新材料设计与研发中得以快速的应用和实践。更重要的是，ALKEMIE2.0设计了用户友好的可视化操作界面，使得结构建模、工作流计算逻辑、数据分析和机器学习模型具有更高的透明性和更强的可操作性，且适用于对材料计算模拟掌握程度从初级到专业的所有材料研究人员。最后，通过多平台部署和高通量筛选二元铝合金2个算例详细展示了ALKEMIE2.0的主要特色及功能。

关键词 ：材料基因工程, 高通量计算, 材料数据库, 人工智能, 多尺度集成计算, 二元铝合金高通量筛选

Abstract：

The development of novel materials has experienced three paradigms: purely empirical, theoretical models, and computational materials science. Currently, the huge amount of data generated by experiments and simulations has facilitated a shift in materials science to a data-driven fourth paradigm. Therefore, the development of high-throughput automatic integrated computations and data mining algorithms based on material databases and artificial intelligence algorithms is critical for accelerating the design of novel materials. This paper presents an open-source distributed computational platform called Artificial Learning and Knowledge Enhanced Materials Informatics Engineering 2.0 (ALKEMIE2.0) based on the AMDIV (automation-modular-database-intelligence-visualization) design concepts. The ALKEMIE2.0 platform includes five core components of automation, modular, materials database, artificial intelligence, and visualization, which are suitable for the computational design of novel materials. The overall characteristics of ALKEMIE2.0 are divided into five pillars. ALKEMIE-Core integrates multiscale calculations and simulation software using the ALKEMIE-Plugin application programming interface. Its high-throughput calculation workflows that support 104 magnitude concurrencies are implemented by integrating the automatic frameworks of model constructions, calculation workflows, and data analyses. Furthermore, the platform is based on the ALKEMIE-Server, which can easily and automatically open daemon services and realize information interactions in distributed supercomputers. With its strong portability and scalability, ALKEMIE has been deployed in the National Supercomputing Tianjin Center. In addition, the multitype materials database called the ALKEMIE-Data Vault contains structure, task, workflow, and material property databases, which combined with the power of supercomputing, enables the rapid application of artificial intelligence algorithms in the design of new materials. In particular, the many user-friendly interfaces, which were elaborately designed using the ALKEMIE-GUI and are suitable for scientists with broad backgrounds, make structural building, work flowcharts, data analysis, and machine learning models more transparent and maneuverable. Finally, the main features of ALKEMIE2.0 are demonstrated using two examples of multiplatform deployment and high-throughput screening of binary aluminum alloys.

Key words： materials genome engineering high-throughput calculation materials database artificial intelligence multi-scale integrated simulation high-throughput screening of binary aluminum alloy

收稿日期: 2021-01-21

ZTFLH:

TB30

基金资助:国家重点研发计划项目(2017YFB0701700);国家自然科学基金项目(51872017);北航高性能计算平台项目

作者简介: 王冠杰，男，1994年生，博士生

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链接本文:

https://www.ams.org.cn/CN/10.11900/0412.1961.2021.00041 或 https://www.ams.org.cn/CN/Y2022/V58/I1/75

图1 ALKEMIE2.0平台的AMDIV设计理念

图2 ALKEMIE2.0系统架构

图3 ALKEMIE2.0平台概况及可视化工作流 (a) the modular (b) work area and workflows of ALKEMIE2.0

图4 高通量自动计算与智能纠错流程图

图5 ALKEMIE2.0数据库类型

图6 铝合金热力学稳定性的高通量筛选，包括未计算的合金化元素(灰色)、能量不稳定的合金化元素(红色)以及能量稳定的合金化元素(白色)，热力学稳定的合金化元素的形成能(负数代表稳定，正数代表不稳定)，及合金元素的平均键长和晶格常数随原子半径的变化散点图

图7 铝合金力学性能和导电性能的高通量筛选

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