AI 中文总结
该研究将人工神经网络逆映射为聚合物连接纳米粒子网络的物理神经网络模型，通过高通量分子动力学模拟和TuRBO方法优化其性能，验证了其可作为基于热的计算器件的潜力。
AI 中文摘要
开发物理神经网络（PNN）硬件对下一代人工智能系统至关重要。声子器件利用热流编码和处理信息，是神经形态计算的解决方案之一。本研究将人工神经网络（ANN）逆映射为聚合物连接纳米粒子（PNNP）的PNN模拟模型。原子模拟结果表明，聚合物连接纳米粒子网络有望利用热流实现信息处理。采用高通量分子动力学（MD）模拟和信任区域贝叶斯优化（TuRBO）方法，调整聚合物连接子的可塑性和纳米粒子温度以优化PNNP机器的性能，这类似于调整ANN的权重和偏置。经过5轮高通量MD模拟，PNNP机器的性能得到提升。还使用测试数据集验证每轮排名前5的PNNP机器的热流输出。
英文摘要
Developing physical neural network (PNN) hardwares is important to next generation artificial intelligence systems. Phononic devices-using heat current to encode and process information-is one of the solutions to neuromorphic computing. In this work, we back map an artificial neural network (ANN) into a PNN simulation model using polymer networked nanoparticles (PNNPs). Our atomistic simulation results demonstrate that the polymer linked nanoparticle networks can potentially realize information processing using heat current. Using high-throughput molecular dynamics (MD) simulations and the trust region Bayesian optimization (TuRBO) methods, we tune the plasticity of polymer linkers and the temperatures of nanoparticles to optimize the performance of the PNNP machines, which is similar to tune the weights and bias in ANNs. After 5 rounds of high-throughput MD simulations, we show that the PNNP machines have improved in performance. We also use a testing data set to verify the heat flow outputs from the top 5 PNNP machines in each round.
