摘要
随着材料基因组计划的提出,以及大数据技术和人工智能技术的飞速发展,基于数据驱动的新材料设计得到了广泛的关注,并逐渐成为新型材料研发的重要方法。近年来,国内外研究人员在基于高通量计算和人工智能技术预测材料组织性能等方面开展了大量工作,获得了大量通过实验方法难以直接获取的性能参数,形成了以数据为核心的材料设计方法。镁合金在航空航天、电子信息和生物医用等领域展现出很好的应用前景,但是强度低、塑性差以及耐腐蚀性能差等不足限制了其进一步应用。概述了近年来国内外研究人员利用基于密度泛函理论的第一性原理计算以及计算机人工智能的机器学习方法,在研究镁合金力学性能及相关组织结构,如热力学稳定性、滑移系启动能垒、成分/组织/工艺与性能关系,以及耐腐蚀性能,如阳极电极电位、功函数计算、阴极表面水解和析氢反应方面的进展,并展望了该研究领域亟待解决的问题以及未来发展方向。
With the proposal of Material Genome Initiative and the rapid development of big data technology and artificial intelligence(AI),data-driven new material design has received widespread attention and gradually become an important method for researching and developing new materials.In recent years,researchers worldwide have carried out a lot of work on high-throughput calculations and AI-based materials design.They have obtained a large number of physical parameters that are difficult to obtain directly by experimental methods.That is the essence of data-driven materials design.Magnesium alloys have shown good application prospects in aerospace,electronic information and biomedical fields,but their low strength,poor plasticity,and low corrosion resistance have limited their further applications.This paper summarizes the recent researches for magnesium alloys based on first-principles calculation of density functional theory and the machine learning method of computer artificial intelligence,focusing on the mechanical properties,structures,microstructures of magnesium alloys(such as thermodynamic stability,energy barriers starting slip systems,relations between mechanical properties and constitutions/processes/microstructures),and corrosion resistance(such as the calculation of anode electrode potential,work function,cathode surface hydrolysis and hydrogen evolution reaction).Finally,the problems needed to be solved in the future are discussed.
作者
曾小勤
谢天
应韬
朱虹
刘言伟
王乐耘
丁文江
ZENG Xiaoqin;XIE Tian;YING Tao;ZHU Hong;LIU Yanwei;WANG Leyun;DING Wenjiang(National Engineering Research Center of Light Alloy Net Forming,School of Materials Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《中国材料进展》
CAS
CSCD
北大核心
2020年第1期1-11,30,共12页
Materials China
基金
国家自然科学基金资助项目(51825101,51171113,51631006)
关键词
镁合金
第一性原理计算
机器学习
力学性能
耐腐蚀性能
magnesium alloy
first-principles calculation
machine learning
mechanical properties
corrosion resistance
