Harnessing machine learning potentials to understand the functional properties of phase-change materials
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Introduction The development of novel nonvolatile memories (NVMs) is key to further our ability to retain, share, and process the evergrowing amount of data generated every day. Current NVMs based on Flash technology suffer from relatively low speeds and limited endurance. Among the alternative options to Flash technology, phase-change memories1,2 stand out as one of the most promising candidates, as attested to by the recent Optane memory, based on the Intel/Micron 3D Xpoint technology that entered the market in 2017 as a storage-class memory.3 In phase-change memories, information is encoded into two different phases of phase-change materials (PCMs) such as chalcogenide alloys,4,5 which can reversibly (up to ∼1012 times)6,7 switch between the crystalline and amorphous phases upon Joule heating within a few nanoseconds (see the article by Kim et al. in this issue7). The two phases have markedly different electrical resistance values that are exploited in the memory readout. Although the Ge2Sb2Te5 compound is presently the material of choice for phase-change memories, the quest for alloys with better performance continues.5,8 For embedded applications in the automotive industry, for instance, data retention above 100°C is desirable, which is not achievable with Ge2Sb2Te5. Other applications such as neuro-inspired computing9 and
photonic devices10 would also benefit from tailoring of the functional properties of phase-change alloys. To this end, a thorough understanding of the microscopic features of PCMs is mandatory. In this regard, atomistic simulations can provide valuable microscopic information that would be difficult to be gained experimentally. First-principles (or ab initio) electronic-structure calculations are usually the tool of the trade, and the field has greatly benefited from molecular dynamics (MD) simulations based on density functional theory (DFT).5,8,11–14 Nonetheless, investigations of many properties of phasechange alloys lie well beyond the capabilities of DFT methods. For instance, the crystallization of amorphous nanowires (a possible alternative architecture for phase-change memories) requires simulations of ∼104 atoms for several nanoseconds, while DFT simulations are typically limited to a few hundred atoms for up to few nanoseconds. For a well-studied material such as silicon, it is straightforward to perform large-scale simulations by picking an empirical/classical potential of choice and by striking some balance between accuracy (some of which would be lost) and computational efficiency. However, even though a classical interatomic potential has been devised for GeTe,15 PCMs display complex interplay between different atomic
G.C. Sosso, Department of Chemistry and Centre for Scientific Computing, University of Warwick, UK; [email protected] M. Bernasconi, Department of Materials Science, University of Milano-Bicocca, Italy; [email protected] doi:10.1557/mrs.2019.202
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