摘要
大数据时代人工智能、5G、云计算等先进技术对数据存储与处理的需求急剧上升,而新型非易失性存储材料与器件的研发则为大幅提升算力提供了契机。同时,人工智能技术驱动的科学研究范式也为进一步提升存储器件性能提供了新的研发模式。本文聚焦于相变存储材料与器件在计算与数据驱动下的研究进展,详细论述了大尺度第一性原理分子动力学、新材料设计与高通量材料筛选、多尺度模拟与机器学习势开发等先进材料计算方法在相变存储材料研究中的具体应用,并展望了相变存储技术发展所面临的机遇与挑战。
In the era of big data,the demand for data storage and processing is increasing because of advanced technologies such as artificial intelligence(AI),5G,and cloud computing.Emerging non-volatile memory materials and devices present remarkable opportunities to enhance computing capacity.Concurrently,the AI-driven scientific research paradigm introduces a new mode for improving device performance.This review focuses on recent advances in phase-change memory materials and devices,emphasizing computational-and data-driven methodologies.Phase-change materials(PCMs)operate based on rapid and reversible phase transitions between amorphous and crystalline states,where differences in electrical and optical properties are used to encode digital information.These materials typically consist of multicomponent alloys,with phase transitions involving melting,quenching,crystallization,glass relaxation,and crystal-crystal structural changes.To achieve a detailed atomistic understanding of PCMs,largescale density functional theory(DFT)and DFT-based ab initio molecular dynamics(AIMD)simulations are essential.Comparisons between DFT/AIMD simulations and experimental results have clarified many fundamental aspects of PCM.The first part of this review provides an overview of the history and progress in large-scale ab initio simulations of PCMs.With atomic-scale knowledge,rational materials design becomes feasible.The second part explores methods for developing new PCMs with specific properties,such as accelerating crystallization at elevated temperatures while maintaining non-volatile characteristics at room temperature.High-throughput screenings role in discovering new phase change alloys is also discussed.In the third part,we examine multiscale and cross-scale simulations of PCM for various optical and electronic phase change applications.By computing the dielectric functions of PCM during the amorphous-to-crystalline transition,we can track changes in the refractive index and extinction coefficient across visible and infrared spect
作者
沈雪阳
褚瑞轩
蒋宜辉
张伟
SHEN Xueyang;CHU Ruixuan;JIANG Yihui;ZHANG Wei(Center for Alloy Innovation and Design(CAID),State Key Laboratory for Mechanical Behavior of Materials,Xi'an Jiaotong University,Xi'an 710049,China)
出处
《金属学报》
SCIE
EI
CAS
CSCD
北大核心
2024年第10期1362-1378,共17页
Acta Metallurgica Sinica
基金
国家重点研发计划项目No.2023YFB4404500
国家自然科学基金项目No.62374131。
关键词
相变存储材料
第一性原理
高通量计算
多尺度模拟
机器学习势
phase-change memory material
first principles
high-throughput screening
multiscale simulation
machine-learning potential
