一个基于非稳定状态电荷传输动力学和其在水库计算中的信息处理能力的一般分子级动态memristor模型
Yueqi Chen1,2, Xuan Ji1,2, Xi Yu1,2
1State Key Laboratory of Advanced Materials for Intelligent Sensing, Key Laboratory of Organic Integrated Circuit, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science, Tianjin University, Tianjin 300072, China.
The Journal of chemical physics
|November 17, 2025
概括
一个新的动态memristor模型整合了电子运输和化学动力学,以模仿大脑功能. 这种方法通过将分子动力学与计算任务对齐来优化神经形态计算性能,为后穆尔时代设备铺平了道路.
科学领域:
- 分子电子学分子电子学
- 神经形态计算是一种神经形态计算.
- 材料科学 是一种材料科学.
背景情况:
- 非稳态分子动力学,涉及合电子运输和化学进化,对于分子记忆器至关重要.
- 目前缺乏将这些动态与神经形态计算联系起来的一般模型.
研究的目的:
- 开发一个集电子运输和缓慢化学动力学的动态memristor模型.
- 证明模型能够复制实验数据并模拟突触功能的能力.
- 探索该模型在储计算中的应用,以实现最佳性能.
主要方法:
- 兰道尔和马库斯电子运输理论与缓慢过程的动力学 (例如,离子迁移,构造变化) 的整合.
- 实验导电性歇斯底里斯和模拟突触可塑性的复制 (短期和尖端时间依赖).
- 将动态模型集成到储库计算架构中.
主要成果:
- 动态的memristor模型成功地复制了实验性的电导率歇斯底里.
- 该模型模拟了关键的突触功能,包括短期和尖端时间依赖的可塑性.
- 当输入频率和偏差映射与分子系统动力学保持一致时,储计算设置中的计算性能会得到优化.
结论:
- 一个以化学为中心的自下而上的动态模型为分子级神经形态计算提供了理论基础.
- 非稳定状态的分子动力学可以有效地驱动未来计算范式的信息处理.
- 这种方法为在后穆尔时代开发先进的神经形态设备提供了一条途径.
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