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硫系相变材料(Phase Change Materials, PCMs)因其独特的电学和光学特性而被广泛研究。PCMs已被成功应用于各类光盘，并在数据存储领域取得显著进展，例如相变随机存取存储器(Phase Change Random Access Memory, PCRAM)。此外，PCMs在光子学领域展现出良好的应用前景，可用于调控光的传播与相互作用；同时也适用于模拟人脑功能的神经拟态计算系统。本综述全面总结了大数据分析与机器学习方法辅助下的PCMs相关研究：高通量计算与机器学习模型可以实现最优掺杂剂筛选与相变材料性能预测；机器学习势函数可以深化对相变材料的相变动力学及热力学特性的理解；在PCRAM器件级模拟与设计中，机器学习在存储器优化及PCMs神经拟态计算潜力挖掘方面发挥了关键作用。最后，总结了PCMs的未来研究方向与当前面临的挑战。
Abstract：Chalcogenide Phase Change Materials(PCMs) have emerged as the cornerstone for advanced data storage and computing architectures, particularly Phase Change Random Access Memory(PCRAM) and neuro-inspired computing systems, due to their rapid and reversible switching between high-resistance amorphous and low-resistance crystalline states. However, the traditional trial-and-error research paradigm is increasingly constrained by long development cycles and high costs. Furthermore, conventional theoretical tools like Density Functional Theory(DFT) are limited by small spatial scales and short simulation times, making it difficult to capture complex phase transition dynamics. This review comprehensively elaborates on how the integration of big data analytics and Machine Learning(ML) has revolutionized PCMs research, transitioning it from empirical observation to precise prediction and directional design. The significant research progress is detailed in three main aspects. High-throughput calculation serves as a data production engine for material property exploration. By automating First-Principles calculations, researchers can rapidly traverse vast compositional spaces. For instance, in the optimization of Sb2 Te3, High-throughput calculation successfully screened Sc and Y as optimal dopants from transition metals. Calculations revealed that Sc reduces lattice mismatch stress, while Y significantly lowers thermal conductivity, thereby enhancing thermal stability and reducing power consumption. Additionally, high-throughput calculation has accelerated the discovery of novel metastable cubic phases in V-VI and IV-V-VI material systems, establishing a precise correlation between microscopic structure and macroscopic performance. Machine Learning Potentials(MLPs) have enabled large-scale molecular dynamics simulations. By training on high-precision DFT datasets, MLPs achieve ab initio accuracy with the computational efficiency of classical force fields. This breakthrough allows for simulations involving hundreds of thousands of atoms over nanosecond timescales, providing unprecedented insights into crystallization kinetics. Key findings include the observation of the breakdown of the Stokes-Einstein relation in supercooled liquid GeTe, where viscosity and diffusion decouple. Furthermore, ML-driven simulations have clarified the distinct crystallization mechanisms of different alloys—identifying Sb2 Te as growth-dominated and Sb2 Te3 as nucleation-dominated. In terms of thermal transport, the combination of MLPs and Non-Equilibrium Molecular Dynamics(NEMD) has successfully quantified the thermal conductivity of amorphous phases and revealed how local structural distortions(e.g., reversible octahedral-heptahedral transformations in Sc-doped PCMs) suppress phonon transport. ML facilitates device-level simulation and inverse design. At the atomic scale, ML models have simulated entire memory cells(exceeding 500 000 atoms), capturing critical phenomena such as element segregation and interfacial diffusion between electrodes and PCMs layers under thermal cycling. Beyond simulation, ML algorithms are applied to the inverse design of functional devices. In neuromorphic computing, ML optimizes synaptic weight updates by leveraging the resistance drift characteristics of PCMs. In photonics, Deep Learning models enable the rapid inverse design of metasurfaces, accurately predicting geometric parameters for desired optical responses(e.g., specific colors or transmission spectra), thereby overcoming the non-uniqueness problem in electromagnetic simulations. Summary and prospects While the convergence of data-driven methods and material science has yielded breakthrough progress, the field remains in a nascent stage with significant challenges. The primary challenge is the data gap. There is a scarcity of standardized, high-quality experimental data at the device level(e.g., real-time resistance drift statistics under operating conditions), which limits the generalization ability of ML models. Secondly, multi-physics coupling is a major hurdle. Current ML simulations predominantly focus on thermal-structural interactions. However, real-world PCRAM operation involves a complex interplay of thermal, electrical(Joule heating, carrier transport), and chemical fields. Developing a unified model that integrates these multi-domain dynamics is crucial for predictive device design. Thirdly, the “black box” nature of ML models often obscures the underlying physical laws, necessitating the development of interpretable AI to validate findings against established physical rules, such as the 8-N rule in chalcogenide glasses. Looking forward, the research focus is expected to shift toward Crystalline-Crystalline Phase Transitions(CCPT). Materials like In2 Se3 and MoTe2, which switch between different crystalline polymorphs, offer superior energy efficiency and stability compared to traditional amorphous-crystalline transitions. ML will play a vital role in accelerating the discovery of such materials. Additionally, Crystal Structure Prediction(CSP) assisted by ML will become a standard tool for exploring unknown metastable phases. Ultimately, the goal is to establish a closed-loop multi-scale modeling framework that connects microscopic atomic behaviors directly to macroscopic device reliability, providing solid theoretical support for the development of low-power, high-endurance, and high-density storage technologies.
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基本信息:
DOI：10.14062/j.issn.0454-5648.20250815
中图分类号:TB34
引用信息:
[1]张烜广,周健,孙志梅.数据驱动与机器学习辅助的硫系相变材料研究进展[J].硅酸盐学报,2026,54(01):70-89.DOI:10.14062/j.issn.0454-5648.20250815.
基金信息:
国家自然科学基金(52332005); 新材料重大专项(2025ZD06 18802)
2026-01-06
2026-01-06
2026-01-06