Department of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
J Chem Phys. 2021 Jun 7;154(21):214702. doi: 10.1063/5.0048898.
Materials that exhibit synaptic properties are a key target for our effort to develop computing devices that mimic the brain intrinsically. If successful, they could lead to high performance, low energy consumption, and huge data storage. A 2D square array of engineered nanoparticles (ENPs) interconnected by an emergent polymer network is a possible candidate. Its behavior has been observed and characterized using coarse-grained molecular dynamics (CGMD) simulations and analytical lattice network models. Both models are consistent in predicting network links at varying temperatures, free volumes, and E-field (E⃗) strengths. Hysteretic behavior, synaptic short-term plasticity and long-term plasticity-necessary for brain-like data storage and computing-have been observed in CGMD simulations of the ENP networks in response to E-fields. Non-volatility properties of the ENP networks were also confirmed to be robust to perturbations in the dielectric constant, temperature, and affine geometry.
表现出突触特性的材料是我们努力开发内在模拟大脑的计算设备的关键目标。如果成功，它们可能会带来高性能、低能耗和巨大的数据存储。由新兴聚合物网络互连的工程纳米粒子 (ENP) 的 2D 方形阵列是一个可能的候选者。已经使用粗粒度分子动力学 (CGMD) 模拟和分析晶格网络模型观察和描述了其行为。这两个模型都一致地预测了在不同温度、自由体积和电场 (E⃗) 强度下的网络链接。在对 ENP 网络的 CGMD 模拟中，观察到了电滞行为、突触短期可塑性和长期可塑性——这是大脑样数据存储和计算所必需的——以响应电场。ENP 网络的非易失性特性也被证实对介电常数、温度和仿射几何的扰动具有鲁棒性。
