Progress on Materials Design and Multiscale Simulations for Phase-Change Memory

doi:10.11900/0412.1961.2024.00188

Acta Metall Sin

2024, Vol. 60

Issue (10): 1362-1378 DOI: 10.11900/0412.1961.2024.00188

Overview

Current Issue | Archive | Adv Search

Progress on Materials Design and Multiscale Simulations for Phase-Change Memory

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

Cite this article:

SHEN Xueyang, CHU Ruixuan, JIANG Yihui, ZHANG Wei. Progress on Materials Design and Multiscale Simulations for Phase-Change Memory. Acta Metall Sin, 2024, 60(10): 1362-1378.

Download:

HTML

PDF(4227KB)
Export: BibTeX | EndNote (RIS)

Abstract

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, large-scale 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 screening's 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 spectra over time. These DFT-computed parameters inform coarse-grained device simulations using finite-difference time-domain (FDTD) or finite element method (FEM). Based on these multiscale simulations, we offer optimization guidelines for non-volatile color display and photonic waveguide devices. The machine learning potentials address some performance gaps between the DFT/AIMD and FEM/FDTD calculations. Machine-learning-driven molecular dynamics (MLMD) simulations serve as cross-scale simulations, with recent developments including neural networks, graph convolutional neural networks, and Gaussian approximation potentials. We discuss the role of MLMD in enabling device-scale atomistic simulations, facilitating device design and optimization with atomic-scale information. Finally, we outline future opportunities and challenges in theoretical PCM research. With ongoing AI-driven fundamental research, we anticipate the commercialization of high-performance phase change memory, neuroinspired computing, and reconfigurable nanophotonic devices, which will, in turn, foster the development of more advanced theoretical tools for research.

Key words: phase-change memory material first principles high-throughput screening multiscale simulation machine-learning potential

Received: 03 June 2024

ZTFLH:

TB303

Fund: National Key Research and Development Program of China(2023YFB4404500);National Natural Science Foundation of China(62374131)

Corresponding Authors: ZHANG Wei, professor, Tel: (029)82664839, E-mail: wzhang0@mail.xjtu.edu.cn

	Service

	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	（）
	Articles by authors
	SHEN Xueyang
	CHU Ruixuan
	JIANG Yihui
	ZHANG Wei

URL:

https://www.ams.org.cn/EN/10.11900/0412.1961.2024.00188 OR https://www.ams.org.cn/EN/Y2024/V60/I10/1362

Fig.1 Commercialized product and working principle of phase-change materials, and artificial intelligence (AI)-driven materials discovery
(a) 3D crosspoint memory^[3] (b) principle of phase-change materials^[34] (c) AI-driven materials discovery^[38]

Fig.2 Ab initio molecular dynamic (AIMD) modeling of amorphous Ge₂Sb₂Te₅ phase-change material (a, b) structure factor (a) and ring lengths (b) distribution calculated at 300 K^[53] ( Q —scattering vector, S( Q )—X-ray scattering factor, a-GST—amorphous GST, ABAB—ABAB rings, A: Ge/Sb, B: Te) (c) evolution of ABAB rings calculated at about 600 K^[56]

Fig.3 AIMD crystallization simulations of a 1008-atom Ge₁Sb₂Te₄ model at about 600 K^[61]
(a) snapshots of the crystallization process identified by

q 4 d o t

(b) snapshots of the crystallization process identified by smooth overlap of atomic positions (SOAP) kernel

k ¯

(

k ¯

—SOAP kernel function)
(c) structural analyses of the crystallization process (V—volume of the crystalline region, S—area of the interface between amorphous and crystalline regions, V_g—crystal growth velocity,

q 4 d o t

—local order parameter)

Fig.4 Design of Sc_0.2Sb₂Te₃ phase-change alloy with ultrafast nucleation rate and device validation^[56]
(a) structure of rocksalt Sb₂Te₃ (E_coh—cohesive energy, a—cell length, the yellow spheres represent the sublattice occupied by Sb atoms and vacancies with a ratio of 2∶1, the blue spheres represent the sublattice occupied by Te atoms)
(b) materials screening (CN—coordination number)
(c) crystallization process
(d) SET speed (GST—Ge₂Sb₂Te₅, SST—Sc_0.2Sb₂Te₃. Inset shows the schematic of the T type phase change device, TEC—top electrode contact, BEC—bottom electrode contact, ϕ—diameter of BEC)

Fig.5 High-throughput screening and stability analysis of disorder-induced Anderson-insulating chalcogenide^[90]
(a) density functional theory (DFT) screening and atomic displacement for the alloys (Max.—maximum, Avg.—average)
(b) structure stability analyses
(c) relaxed models of the alloys

Fig.6 Multiscale simulation of Sb₂Te₁ phase change alloy for waveguide devices ^[111]
(a) local structural snapshots of amorphous and crystalline Sb₂Te₁ (amor.—amorphous; crys.—crystalline; Sb and Te atoms are rendered as yellow and green spheres, respectively)
(b) changes in dielectric function upon crystallization and Te ordering (ε₂—imaginary part of the dielectric function, I_Te—concentration of Te atoms in Te-rich layers)
(c) schematic of a Sb₂Te₁ waveguide device (ITO—indium tin oxide,

h I T O

—height of ITO,

h S b 2 T e 1

—height of Sb₂Te₁ layer,

d S b 2 T e 1

—length of Sb₂Te₁ layer, w_wg—width of the waveguide, h_wg—height of the waveguide, T—transmittance, P₁—power of incident light, P₂—power of transmitted light)
(d) transmittance profiles of different phases
(e) corresponding electric field |E| of the x-z plane of Sb₂Te₁ device in different phases, the color bar below shows the normalized electric field intensity (PCM—phase-change materials)

Fig.7 Process and method for fitting machine learning interatomic potential
(a) construction process of machine learning interatomic potential, according to Ref.[97] (

ξ

—process of getting the smooth overlap of atomic positions descriptor, σ_at—smooth parameter, r_cut—specified cutoff, ρ_i (r_cut)—obtained atomic density, q—descriptors, E(q)—energies; the red, blue and yellow spheres represent Ge, Te and Sb atoms, respectively)
(b) sketch of the neural, networks according to Ref.[118] ( G₁, G₂—input vectors describing the atomic configurations,

γ j i

—weight sum of the node values, α^ij —connecting weight parameter, ɛ—potential energy)
(c) schematic of the graph convolutional neural networks^[119]
(d) schematic of the kernel based Gaussian approximation potential, according to Ref.[37] (E_i —potential energy, α_i —regression coefficient, k—SOAP similarity)

Fig.8 Graph convolutional machine learning potential for binary Sb-Te alloy^[121] (R_c—cutoff radiaus; G —graph descriptor; E—potential energy; F_x, F_y, F_z —atomic forces of x, y, z components; NN—neural network)

Fig.9 Device-scale atomistic modeling of Gaussian approximation machine learning potential for ternary Ge₁Sb₂Te₄ alloy^[100]
(a) 3D crosspoint structure (OTS—ovonic threshold switching)
(b) non-isothermal melting process
(c) heat dissipation process (T—temperature)
(d) changes in the radial distribution function (RDF) upon melt-quench amorphization.

1	Wuttig M, Yamada N. Phase-change materials for rewriteable data storage [J]. Nat. Mater., 2007, 6: 824 pmid: 17972937
2	Wong H S P, Raoux S, Kim S, et al. Phase change memory [J]. Proc. IEEE, 2010, 98: 2201
3	Zhang W, Mazzarello R, Wuttig M, et al. Designing crystallization in phase-change materials for universal memory and neuro-inspired computing [J]. Nat. Rev. Mater., 2019, 4: 150 doi: 10.1038/s41578-018-0076-x
4	Zhou W, Shen X Y, Yang X L, et al. Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing [J]. Int. J. Extrem. Manuf., 2024, 6: 022001
5	Zhang Z H, Wang Z W, Shi T, et al. Memory materials and devices: From concept to application [J]. InfoMat, 2020, 2: 261
6	Kwon D H, Kim K M, Jang J H, et al. Atomic structure of conducting nanofilaments in TiO₂ resistive switching memory [J]. Nat. Nanotechnol., 2010, 5: 148
7	Prezioso M, Merrikh-Bayat F, Hoskins B D, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors [J]. Nature, 2015, 521: 61
8	Liu S, Lu N D, Zhao X L, et al. Eliminating negative-SET behavior by suppressing nanofilament overgrowth in cation-based memory [J]. Adv. Mater., 2016, 28: 10623
9	Mangin S, Ravelosona D, Katine J A, et al. Current-induced magnetization reversal in nanopillars with perpendicular anisotropy [J]. Nat. Mater., 2006, 5: 210
10	Torrejon J, Riou M, Araujo F A, et al. Neuromorphic computing with nanoscale spintronic oscillators [J]. Nature, 2017, 547: 428
11	Park B H, Kang B S, Bu S D, et al. Lanthanum-substituted bismuth titanate for use in non-volatile memories [J]. Nature, 1999, 401: 682
12	Chanthbouala A, Garcia V, Cherifi R O, et al. A ferroelectric memristor [J]. Nat. Mater., 2012, 11: 860 doi: 10.1038/nmat3415 pmid: 22983431
13	Wong H S P, Salahuddin S. Memory leads the way to better computing [J]. Nat. Nanotechnol., 2015, 10: 191 doi: 10.1038/nnano.2015.29 pmid: 25740127
14	Yang J J, Strukov D B, Stewart D R. Memristive devices for computing [J]. Nat. Nanotechnol., 2013, 8: 13 doi: 10.1038/nnano.2012.240 pmid: 23269430
15	Service R F. The brain chip [J]. Science, 2014, 345: 614 doi: 10.1126/science.345.6197.614 pmid: 25104367
16	Lanza M, Sebastian A, Lu W D, et al. Memristive technologies for data storage, computation, encryption, and radio-frequency communication [J]. Science, 2022, 376: eabj9979
17	Liu B, Wei T, Hu J, et al. Universal memory based on phase-change materials: From phase-change random access memory to optoelectronic hybrid storage [J]. Chin. Phys., 2021, 30B: 058504
18	Kau D, Tang S, Karpov I V, et al. A stackable cross point phase change memory [A]. Proceedings of 2009 IEEE International Electron Devices Meeting [C]. Baltimore: IEEE, 2009: 1
19	Cheng H Y, Carta F, Chien W C, et al. 3D cross-point phase-change memory for storage-class memory [J]. J. Phys., 2019, 52D: 473002
20	Li X, Chen H P, Xie C C, et al. Enhancing the performance of phase change memory for embedded applications [J]. Phys. Status Solidi (RRL), 2019, 13: 1800558
21	Arnaud F, Ferreira P, Piazza F, et al. High density embedded PCM cell in 28 nm FDSOI technology for automotive micro-controller applications [A]. Proceedings of 2020 IEEE International Electron Devices Meeting [C]. San Francisco: IEEE, 2020: 24.2.1
22	Cappelletti P, Annunziata R, Arnaud F, et al. Phase change memory for automotive grade embedded NVM applications [J]. J. Phys., 2020, 53D: 193002
23	Sebastian A, Le Gallo M, Khaddam-Aljameh R, et al. Memory devices and applications for in-memory computing [J]. Nat. Nanotechnol., 2020, 15: 529 doi: 10.1038/s41565-020-0655-z pmid: 32231270
24	Xu M, Mai X L, Lin J, et al. Recent advances on neuromorphic devices based on chalcogenide phase-change materials [J]. Adv. Funct. Mater., 2020, 30: 2003419
25	Shastri B J, Tait A N, Ferreira de Lima T, et al. Photonics for artificial intelligence and neuromorphic computing [J]. Nat. Photonics, 2021, 15: 102
26	Liu H L, Dong W L, Wang H, et al. Rewritable color nanoprints in antimony trisulfide films [J]. Sci. Adv., 2020, 6: eabb7171
27	Cheng Z G, Milne T, Salter P, et al. Antimony thin films demonstrate programmable optical nonlinearity [J]. Sci. Adv., 2021, 7: eabd7097
28	Ríos C, Stegmaier M, Hosseini P, et al. Integrated all-photonic non-volatile multi-level memory [J]. Nat. Photonics, 2015, 9: 725 doi: 10.1038/NPHOTON.2015.182
29	Cheng Z G, Ríos C, Pernice W H P, et al. On-chip photonic synapse [J]. Sci. Adv., 2017, 3: e1700160
30	Wuttig M, Bhaskaran H, Taubner T. Phase-change materials for non-volatile photonic applications [J]. Nat. Photonics, 2017, 11: 465
31	Wu C M, Yu H S, Lee S, et al. Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network [J]. Nat. Commun., 2021, 12: 96 doi: 10.1038/s41467-020-20365-z pmid: 33398011
32	Ríos C, Youngblood N, Cheng Z G, et al. In-memory computing on a photonic platform [J]. Sci. Adv., 2019, 5: eaau5759
33	Feldmann J, Youngblood N, Karpov M, et al. Parallel convolutional processing using an integrated photonic tensor core [J]. Nature, 2021, 589: 52
34	Zhang W, Mazzarello R, Ma E. Phase-change materials in electronics and photonics [J]. MRS Bull., 2019, 44: 686 doi: 10.1557/mrs.2019.201
35	Curtarolo S, Hart G L W, Nardelli M B, et al. The high-throughput highway to computational materials design [J]. Nat. Mater., 2013, 12: 191 doi: 10.1038/nmat3568 pmid: 23422720
36	Xie J X, Su Y J, Xue D Z, et al. Machine learning for materials research and development [J]. Acta Metall. Sin., 2021, 57: 1343 doi: 10.11900/0412.1961.2021.00357
	谢建新, 宿彦京, 薛德祯等. 机器学习在材料研发中的应用 [J]. 金属学报, 2021, 57: 1343 doi: 10.11900/0412.1961.2021.00357
37	Friederich P, Häse F, Proppe J, et al. Machine-learned potentials for next-generation matter simulations [J]. Nat. Mater., 2021, 20: 750 doi: 10.1038/s41563-020-0777-6 pmid: 34045696
38	Li H, Xu Y, Duan W H. Ab initio artificial intelligence: Future research of Materials Genome Initiative [J]. MGE Adv., 2023, 1: e16
39	Su Y J, Fu H D, Bai Y, et al. Progress in materials genome engineering in China [J]. Acta Metall. Sin., 2020, 56: 1313 doi: 10.11900/0412.1961.2020.00199
	宿彦京, 付华栋, 白洋等. 中国材料基因工程研究进展 [J]. 金属学报, 2020, 56: 1313 doi: 10.11900/0412.1961.2020.00199
40	Xie J X. Prospects of materials genome engineering frontiers [J]. MGE Adv., 2023, 1: e17
41	Feng R, Zhang C, Gao M C, et al. High-throughput design of high-performance lightweight high-entropy alloys [J]. Nat. Commun., 2021, 12: 4329 doi: 10.1038/s41467-021-24523-9 pmid: 34267192
42	Rao Z Y, Tung P Y, Xie R W, et al. Machine learning-enabled high-entropy alloy discovery [J]. Science, 2022, 378: 78 doi: 10.1126/science.abo4940 pmid: 36201584
43	Xu Y F, Elcoro L, Song Z D, et al. High-throughput calculations of magnetic topological materials [J]. Nature, 2020, 586: 702
44	Choudhary K, Garrity K F, Jiang J, et al. Computational search for magnetic and non-magnetic 2D topological materials using unified spin-orbit spillage screening [J]. npj Comput. Mater., 2020, 6: 49
45	Gorai P, Stevanović V, Toberer E S. Computationally guided discovery of thermoelectric materials [J]. Nat. Rev. Mater., 2017, 2: 17053
46	Wang X D, Tan J L, Ouyang J, et al. Designing inorganic semiconductors with cold-rolling processability [J]. Adv. Sci., 2022, 9: 2203776
47	Gao Z Q, Wei T R, Deng T T, et al. High-throughput screening of 2D van der Waals crystals with plastic deformability [J]. Nat. Commun., 2022, 13: 7491 doi: 10.1038/s41467-022-35229-x pmid: 36470897
48	Jain A, Ong S P, Hautier G, et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation [J]. APL Mater., 2013, 1: 011002
49	Hicks D, Toher C, Ford D C, et al. AFLOW-XtalFinder: A reliable choice to identify crystalline prototypes [J]. npj Comput. Mater., 2021, 7: 30
50	Wang G J, Li K Q, Peng L Y, et al. High-throughput automatic integrated material calculations and data management intelligent platform and the application in novel alloys [J]. Acta Metall. Sin., 2022, 58: 75 doi: 10.11900/0412.1961.2021.00041
	王冠杰, 李开旗, 彭力宇等. 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: 75 doi: 10.11900/0412.1961.2021.00041
51	Yamada N, Ohno E, Akahira N, et al. High speed overwritable phase change optical disk material [J]. Jpn. J. Appl. Phys., 1987, 26(s4): 61
52	Yamada N, Ohno E, Nishiuchi K, et al. Rapid-phase transitions of GeTe-Sb₂Te₃pseudobinary amorphous thin films for an optical disk memory [J]. J. Appl. Phys., 1991, 69: 2849
53	Caravati S, Bernasconi M, Kühne T D, et al. Coexistence of tetrahedral- and octahedral-like sites in amorphous phase change materials [J]. Appl. Phys. Lett., 2007, 91: 171906
54	Akola J, Jones R O. Structural phase transitions on the nanoscale: The crucial pattern in the phase-change materials Ge₂Sb₂Te₅ and GeTe [J]. Phys. Rev., 2007, 76B: 235201
55	Kohara S, Kato K, Kimura S, et al. Structural basis for the fast phase change of Ge₂Sb₂Te₅: Ring statistics analogy between the crystal and amorphous states [J]. Appl. Phys. Lett., 2006, 89: 201910
56	Rao F, Ding K Y, Zhou Y X, et al. Reducing the stochasticity of crystal nucleation to enable subnanosecond memory writing [J]. Science, 2017, 358: 1423 doi: 10.1126/science.aao3212 pmid: 29123020
57	Orava J, Greer A L, Gholipour B, et al. Characterization of supercooled liquid Ge₂Sb₂Te₅ and its crystallization by ultrafast-heating calorimetry [J]. Nat. Mater., 2012, 11: 279 doi: 10.1038/nmat3275 pmid: 22426461
58	Jeyasingh R, Fong S W, Lee J, et al. Ultrafast characterization of phase-change material crystallization properties in the melt-quenched amorphous phase [J]. Nano Lett., 2014, 14: 3419 doi: 10.1021/nl500940z pmid: 24798660
59	Ronneberger I, Zhang W, Eshet H, et al. Crystallization properties of the Ge₂Sb₂Te₅ phase-change compound from advanced simulations [J]. Adv. Funct. Mater., 2015, 25: 6407
60	Ronneberger I, Zhang W, Mazzarello R. Crystal growth of Ge₂Sb₂Te₅ at high temperatures [J]. MRS Commun., 2018, 8: 1018
61	Xu Y Z, Zhou Y X, Wang X D, et al. Unraveling crystallization mechanisms and electronic structure of phase-change materials by large-scale ab initio simulations [J]. Adv. Mater., 2022, 34: 2109139
62	ten Wolde P R, Ruiz-Montero M J, Frenkel D. Simulation of homogeneous crystal nucleation close to coexistence [J]. Faraday Discuss., 1996, 104: 93
63	Bartók A P, Csányi G. Gaussian approximation potentials: A brief tutorial introduction [J]. Int. J. Quantum Chem., 2015, 115: 1051
64	Kalikka J, Akola J, Jones R O. Crystallization processes in the phase change material Ge₂Sb₂Te₅: Unbiased density functional/molecular dynamics simulations [J]. Phys. Rev., 2016, 94B: 134105
65	Raty J Y, Zhang W, Luckas J, et al. Aging mechanisms in amorphous phase-change materials [J]. Nat. Commun., 2015, 6: 7467 doi: 10.1038/ncomms8467 pmid: 26105012
66	Li X B, Liu X Q, Liu X, et al. Role of electronic excitation in the amorphization of Ge-Sb-Te alloys [J]. Phys. Rev. Lett., 2011, 107: 015501
67	Zalden P, Quirin F, Schumacher M, et al. Femtosecond X-ray diffraction reveals a liquid-liquid phase transition in phase-change materials [J]. Science, 2019, 364: 1062 doi: 10.1126/science.aaw1773 pmid: 31197008
68	Kalikka J, Akola J, Larrucea J, et al. Nucleus-driven crystallization of amorphous Ge₂Sb₂Te₅: A density functional study [J]. Phys. Rev., 2012, 86B: 144113
69	Kalikka J, Akola J, Jones R O. Simulation of crystallization in Ge₂Sb₂Te₅: A memory effect in the canonical phase-change material [J]. Phys. Rev., 2014, 90B: 184109
70	Loke D, Lee T H, Wang W J, et al. Breaking the speed limits of phase-change memory [J]. Science, 2012, 336: 1566 doi: 10.1126/science.1221561 pmid: 22723419
71	Zhang B, Zhang W, Shen Z J, et al. Element-resolved atomic structure imaging of rocksalt Ge₂Sb₂Te₅ phase-change material [J]. Appl. Phys. Lett., 2016, 108: 191902
72	Xu M, Zhang W, Mazzarello R, et al. Disorder control in crystalline GeSb₂Te₄ using high pressure [J]. Adv. Sci., 2015, 2: 1500117
73	Zheng Y H, Xia M J, Cheng Y, et al. Direct observation of metastable face-centered cubic Sb₂Te₃ crystal [J]. Nano Res., 2016, 9: 3453
74	Li Z, Si C, Zhou J, et al. Yttrium-doped Sb₂Te₃: A promising material for phase-change memory [J]. ACS Appl. Mater. Interfaces, 2016, 8: 26126
75	Liu B, Li K Q, Liu W L, et al. Multi-level phase-change memory with ultralow power consumption and resistance drift [J]. Sci. Bull., 2021, 66: 2217 doi: 10.1016/j.scib.2021.07.018 pmid: 36654113
76	Zhou Y X, Sun L, Zewdie G M, et al. Bonding similarities and differences between Y-Sb-Te and Sc-Sb-Te phase-change memory materials [J]. J. Mater. Chem., 2020, 8C: 3646
77	Chen B, Chen Y M, Ding K Y, et al. Kinetics features conducive to cache-type nonvolatile phase-change memory [J]. Chem. Mater., 2019, 31: 8794 doi: 10.1021/acs.chemmater.9b02598
78	Hu S W, Liu B, Li Z, et al. Identifying optimal dopants for Sb₂Te₃ phase-change material by high-throughput ab initio calculations with experiments [J]. Comput. Mater. Sci., 2019, 165: 51
79	Zewdie G M, Zhou Y X, Sun L, et al. Chemical design principles for cache-type Sc-Sb-Te phase-change memory materials [J]. Chem. Mater., 2019, 31: 4008
80	Wang X P, Li X B, Chen N K, et al. Time-dependent density-functional theory molecular-dynamics study on amorphization of Sc-Sb-Te alloy under optical excitation [J]. npj Comput. Mater., 2020, 6: 31
81	Qiao C, Guo Y R, Wang S Y, et al. Local structure origin of ultrafast crystallization driven by high-fidelity octahedral clusters in amorphous Sc_0.2Sb₂Te₃ [J]. Appl. Phys. Lett., 2019, 114: 071901
82	Ding K Y, Chen B, Chen Y M, et al. Recipe for ultrafast and persistent phase-change memory materials [J]. NPG Asia Mater., 2020, 12: 63
83	Ding K Y, Wang J J, Zhou Y X, et al. Phase-change heterostructure enables ultralow noise and drift for memory operation [J]. Science, 2019, 366: 210 doi: 10.1126/science.aay0291 pmid: 31439757
84	Yang Z, Li B W, Wang J J, et al. Designing conductive-bridge phase-change memory to enable ultralow programming power [J]. Adv. Sci., 2022, 9: 2103478
85	Wang J J, Wang X Z, Cheng Y D, et al. Tailoring the oxygen concentration in Ge-Sb-O alloys to enable femtojoule-level phase-change memory operations [J]. Mater. Futures, 2022, 1: 045302
86	Liu Y T, Li X B, Zheng H, et al. High-throughput screening for phase-change memory materials [J]. Adv. Funct. Mater., 2021, 31: 2009803
87	Zhang W, Zhang H M, Sun S Y, et al. Metavalent bonding in layered phase-change memory materials [J]. Adv. Sci., 2023, 10: 2300901
88	Wuttig M, Deringer V L, Gonze X, et al. Incipient metals: Functional materials with a unique bonding mechanism [J]. Adv. Mater., 2018, 30: 1803777
89	Wuttig M, Schön C F, Lötfering J, et al. Revisiting the nature of chemical bonding in chalcogenides to explain and design their properties [J]. Adv. Mater., 2023, 35: 2208485
90	Xu Y Z, Wang X D, Zhang W, et al. Materials screening for disorder-controlled chalcogenide crystals for phase-change memory applications [J]. Adv. Mater., 2021, 33: 2006221
91	Zhang W, Thiess A, Zalden P, et al. Role of vacancies in metal-insulator transitions of crystalline phase-change materials [J]. Nat. Mater., 2012, 11: 952 doi: 10.1038/nmat3456 pmid: 23064498
92	Jiang T T, Wang X D, Wang J J, et al. In situ characterization of vacancy ordering in Ge-Sb-Te phase-change memory alloys [J]. Fundam. Res., 2022, DOI: 10.1016/j.fmre.2022.09.010
93	Esser M, Deringer V L, Wuttig M, et al. Orbital mixing in solids as a descriptor for materials mapping [J]. Solid State Commun., 2015, 203: 31
94	Wang Y Z, Ning J, Lu L, et al. A scheme for simulating multi-level phase change photonics materials [J]. npj Comput. Mater., 2021, 7: 183
95	Khan A I, Daus A, Islam R, et al. Ultralow-switching current density multilevel phase-change memory on a flexible substrate [J]. Science, 2021, 373: 1243 doi: 10.1126/science.abj1261 pmid: 34516795
96	Ghazi Sarwat S, Philip T M, Chen C T, et al. Projected mushroom type phase-change memory [J]. Adv. Funct. Mater., 2021, 31: 2106547
97	Deringer V L, Caro M A, Csányi G. Machine learning interatomic potentials as emerging tools for materials science [J]. Adv. Mater., 2019, 31: 1902765
98	Wang H, Zhang L F, Han J Q, et al. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics [J]. Comput. Phys. Commun., 2018, 228: 178
99	Wang G J, Wang C R, Zhang X G, et al. Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations [J]. iScience, 2024, 27: 109673
100	Zhou Y X, Zhang W, Ma E, et al. Device-scale atomistic modelling of phase-change memory materials [J]. Nat. Electron., 2023, 6: 746
101	Du K K, Li Q, Lyu Y B, et al. Control over emissivity of zero-static-power thermal emitters based on phase-changing material GST [J]. Light Sci. Appl., 2017, 6: e16194
102	Qu Y R, Li Q, Cai L, et al. Thermal camouflage based on the phase-changing material GST [J]. Light Sci. Appl., 2018, 7: 26
103	Li C L, Liu D J, Dai D X. Multimode silicon photonics [J]. Nanophotonics, 2019, 8: 227 doi: 10.1515/nanoph-2018-0161
104	Hosseini P, Wright C D, Bhaskaran H. An optoelectronic framework enabled by low-dimensional phase-change films [J]. Nature, 2014, 511: 206
105	Wang D N, Zhao L, Yu S Y, et al. Non-volatile tunable optics by design: From chalcogenide phase-change materials to device structures [J]. Mater. Today, 2023, 68: 334
106	Siegrist T, Jost P, Volker H, et al. Disorder-induced localization in crystalline phase-change materials [J]. Nat. Mater., 2011, 10: 202 doi: 10.1038/nmat2934 pmid: 21217692
107	Hu C Q, Yang Z B, Bi C B, et al. “All-crystalline” phase transition in nonmetal doped germanium-antimony-tellurium films for high-temperature non-volatile photonic applications [J]. Acta Mater., 2020, 188: 121
108	Matsunaga T, Umetani Y, Yamada N. Structural study of a Ag_3.4In_3.7Sb_76.4Te_16.5 quadruple compound utilized for phase-change optical disks [J]. Phys. Rev., 2001, 64B: 184116
109	Matsunaga T, Akola J, Kohara S, et al. From local structure to nanosecond recrystallization dynamics in AgInSbTe phase-change materials [J]. Nat. Mater., 2011, 10: 129 doi: 10.1038/nmat2931 pmid: 21217690
110	Zhang W, Ronneberger I, Zalden P, et al. How fragility makes phase-change data storage robust: Insights from ab initio simulations [J]. Sci. Rep., 2014, 4: 6529 doi: 10.1038/srep06529 pmid: 25284316
111	Wang X D, Zhou W, Zhang H M, et al. Multiscale simulations of growth-dominated Sb₂Te phase-change material for non-volatile photonic applications [J]. npj Comput. Mater., 2023, 9: 136
112	Wang V, Xu N, Liu J C, et al. VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code [J]. Comput. Phys. Commun., 2021, 267: 108033
113	IncAnsys. Lumerical FDTD solutions [EB/OL].
114	Zheng Y H, Song W X, Song Z T, et al. A complicated route from disorder to order in antimony-tellurium binary phase change materials [J]. Adv. Sci., 2024, 11: 2301021
115	Belsky A J, Hellenbrandt M, Karen V L, et al. New developments in the Inorganic Crystal Structure Database (ICSD): Accessibility in support of materials research and design [J]. Acta Crystallogr., 2002, 58B: 364
116	Pickard C J, Needs R J. Ab initio random structure searching [J]. J. Phys.: Condens. Matter, 2011, 23: 053201
117	Thompson A P, Aktulga H M, Berger R, et al. LAMMPS—A flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales [J]. Comput. Phys. Commun., 2022, 271: 108171
118	Behler J. First principles neural network potentials for reactive simulations of large molecular and condensed systems [J]. Angew. Chem. Int. Ed., 2017, 56: 12828 doi: 10.1002/anie.201703114 pmid: 28520235
119	Xie T, Grossman J C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties [J]. Phys. Rev. Lett., 2018, 120: 145301
120	Behler J. Atom-centered symmetry functions for constructing high-dimensional neural network potentials [J]. J. Chem. Phys., 2011, 134: 074106
121	Wang G J, Sun Y Q, Zhou J, et al. PotentialMind: Graph convolutional machine learning potential for Sb-Te binary compounds of multiple stoichiometries [J]. J. Phys. Chem., 2023, 127C: 24724
122	Bartók A P, Payne M C, Kondor R, et al. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons [J]. Phys. Rev. Lett., 2010, 104: 136403
123	Deringer V L, Bartók A P, Bernstein N, et al. Gaussian process regression for materials and molecules [J]. Chem. Rev., 2021, 121: 10073 doi: 10.1021/acs.chemrev.1c00022 pmid: 34398616
124	Sosso G C, Miceli G, Caravati S, et al. Neural network interatomic potential for the phase change material GeTe [J]. Phys. Rev., 2012, 85B: 174103
125	Sosso G C, Miceli G, Caravati S, et al. Fast crystallization of the phase change compound GeTe by large-scale molecular dynamics simulations [J]. J. Phys. Chem. Lett., 2013, 4: 4241 doi: 10.1021/jz402268v pmid: 26296172
126	Gabardi S, Baldi E, Bosoni E, et al. Atomistic simulations of the crystallization and aging of GeTe nanowires [J]. J. Phys. Chem., 2017, 121C: 23827
127	Abou El Kheir O, Bonati L, Parrinello M, et al. Unraveling the crystallization kinetics of the Ge₂Sb₂Te₅ phase change compound with a machine-learned interatomic potential [J]. npj Comput. Mater., 2024, 10: 33
128	Dragoni D, Behler J, Bernasconi M. Mechanism of amorphous phase stabilization in ultrathin films of monoatomic phase change material [J]. Nanoscale, 2021, 13: 16146
129	Shi M C, Li J H, Tao M, et al. Artificial intelligence model for efficient simulation of monatomic phase change material antimony [J]. Mater. Sci. Semicond. Process., 2021, 136: 106146
130	Li K Q, Liu B, Zhou J, et al. Revealing the crystallization dynamics of Sb-Te phase change materials by large-scale simulations [J]. J. Mater. Chem., 2024, 12C: 3897
131	Mocanu F C, Konstantinou K, Lee T H, et al. Modeling the phase-change memory material, Ge₂Sb₂Te₅, with a machine-learned interatomic potential [J]. J. Phys. Chem., 2018, 122B: 8998
132	Mocanu F C, Konstantinou K, Elliott S R. Quench-rate and size-dependent behaviour in glassy Ge₂Sb₂Te₅ models simulated with a machine-learned Gaussian approximation potential [J]. J. Phys., 2020, 53D: 244002
133	Konstantinou K, Mocanu F C, Lee T H, et al. Revealing the intrinsic nature of the mid-gap defects in amorphous Ge₂Sb₂Te₅ [J]. Nat. Commun., 2019, 10: 3065
134	Konstantinou K, Mocanu F C, Akola J, et al. Electric-field-induced annihilation of localized gap defect states in amorphous phase-change memory materials [J]. Acta Mater., 2022, 223: 117465
135	Zhou Y X, Zhang W, Ma E, et al. Device-scale atomistic modelling of phase-change memory materials [J]. Nat. Electron., 2023, 6: 746
136	Mo P H, Li C, Zhao D, et al. Accurate and efficient molecular dynamics based on machine learning and non von Neumann architecture [J]. npj Comput. Mater., 2022, 8: 107
137	Li H, Wang Z, Zou N L, et al. Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation [J]. Nat. Comput. Sci., 2022, 2: 367
138	Gong X X, Li H, Zou N L, et al. General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian [J]. Nat. Commun., 2023, 14: 2848 doi: 10.1038/s41467-023-38468-8 pmid: 37208320

[1]	CHENG Kun, CHEN Shuming, CAO Shuo, LIU Jianrong, MA Yingjie, FAN Qunbo, CHENG Xingwang, YANG Rui, HU Qingmiao. Precipitation Strengthening in Titanium Alloys from First Principles Investigation[J]. 金属学报, 2024, 60(4): 537-547.
[2]	WANG Guanjie, LI Kaiqi, PENG Liyu, ZHANG Yiming, ZHOU Jian, SUN Zhimei. High-Throughput Automatic Integrated Material Calculations and Data Management Intelligent Platform and the Application in Novel Alloys[J]. 金属学报, 2022, 58(1): 75-88.
[3]	Gang ZHOU, Lihua YE, Hao WANG, Dongsheng XU, Changgong MENG, Rui YANG. A First-Principles Study on Basal/Prismatic Reorientation-Induced Twinning Path and Alloying Effect in Hexagonal Metals[J]. 金属学报, 2018, 54(4): 603-612.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed