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记忆材料的全周期设备规模模拟,具有量身定制的原子集群扩张潜力
Yuxing Zhou1, Daniel F Thomas du Toit1, Stephen R Elliott2
1Inorganic Chemistry Laboratory, Department of Chemistry, University of Oxford, Oxford, UK.
Nature communications
|September 30, 2025
概括
超快速的机器学习潜能使存储器设备的相变材料 (PCM) 的大规模模拟成为可能. 这一突破允许完全编程周期的模拟,这对于推进PCM技术至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 计算机工程 计算机工程
背景情况:
- 计算机模拟对于记忆技术中的相变材料 (PCM) 设计至关重要.
- 机器学习加速PCM建模,但与现实世界的设备长度和时间尺度作斗争.
研究的目的:
- 为可扩展的PCM模拟展示超快速机器学习的原子间潜力.
- 为了实现PCM设备的全周期模拟,包括结晶和操作.
主要方法:
- 利用原子集群扩展 (ACE) 框架用于机器学习的原子间潜力.
- 在高性能计算平台上执行全周期模拟.
主要成果:
- 在HPC平台上实现了PCM模拟的卓越可扩展性.
- 成功模拟了跨点记忆器件的整个编程周期 (从'零'到'一'的结晶).
- 展示了在类设备中用于神经形态计算的全周期运行模拟.
结论:
- 基于ACE的超快速机器学习潜力克服了PCM模拟中的规模限制.
- 这种方法使PCM设备操作和编程周期的真实模拟成为可能.
- 促进了基于PCM的记忆和神经形态计算应用的进步.
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