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• W3092825488 abstract "Studies of supercooled liquid phase‐change materials are important for the development of phase‐change memory and neuromorphic computing devices. Herein, a machine‐learning (ML)‐based interatomic potential for Ge 2 Sb 2 Te 5 (GST) to conduct large‐scale molecular dynamics simulations of liquid and supercooled liquid GST is used. A pronounced effect of the thermostat parameters on the simulation results is demonstrated, and it is shown how using a Langevin thermostat with optimized damping values can lead to excellent agreement with reference ab initio molecular dynamics (AIMD) simulations. Structural and dynamical analyses are presented, including the studies of radial and angular distributions, homopolar bonds, and the temperature‐dependent diffusivity. Herein, the usefulness of ML‐driven molecular dynamics for further studies of supercooled liquid GST, with length and timescales far exceeding those that are accessible to AIMD is demonstrated." @default.
• W3092825488 created "2020-10-22" @default.
• W3092825488 creator A5047079466 @default.
• W3092825488 creator A5055231928 @default.
• W3092825488 creator A5067850823 @default.
• W3092825488 creator A5084438362 @default.
• W3092825488 date "2020-10-22" @default.
• W3092825488 modified "2023-09-26" @default.
• W3092825488 title "Structure and Dynamics of Supercooled Liquid Ge₂Sb₂Te₅ from Machine‐Learning‐Driven Simulations" @default.
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• W3092825488 doi "https://doi.org/10.1002/pssr.202000403" @default.
• W3092825488 hasPublicationYear "2020" @default.
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