Advances in artificial intelligence are driven by technologies inspired by the brain, but these technologies are orders of magnitude less powerful and energy efficient than biological systems. Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged with the potential to exploit natural phenomena and gain efficiency, in a similar manner to biological systems. Physical reservoir computing demonstrates this with a variety of unconventional systems, from optical-based to memristive systems. Reservoir computers provide a nonlinear projection of the task input into a high-dimensional feature space by exploiting the system's internal dynamics. A trained readout layer then combines features to perform tasks, such as pattern recognition and time-series analysis. Despite progress, achieving state-of-the-art performance without external signal processing to the reservoir remains challenging. Here we perform an initial exploration of three magnetic materials in thin-film geometries via microscale simulation. Our results reveal that basic spin properties of magnetic films generate the required nonlinear dynamics and memory to solve machine learning tasks (although there would be practical challenges in exploiting these particular materials in physical implementations). The method of exploration can be applied to other materials, so this work opens up the possibility of testing different materials, from relatively simple (alloys) to significantly complex (antiferromagnetic reservoirs).

翻译：人工智能的进步源于受大脑启发的技术，但这些技术的计算能力和能效比生物系统低数个数量级。受神经网络非线性动力学的启发，新型非传统计算硬件应运而生，其具备利用自然现象获取效率的潜力，与生物系统的运作方式相似。物理储层计算通过多种非传统系统（从光学系统到忆阻系统）展示了这一特性。储层计算机利用系统内部动力学，将任务输入非线性投影至高维特征空间，随后训练好的读出层整合特征以执行模式识别与时间序列分析等任务。尽管取得了进展，但在不借助外部信号处理的情况下实现最先进性能仍具挑战。本文通过微尺度模拟对三种薄膜构型磁性材料开展了初步探索。结果表明，磁性薄膜的基本自旋特性能够产生解决机器学习任务所需的非线性动力学与记忆性（尽管在实际物理实现中利用这些特定材料仍存在实践挑战）。该探索方法可推广至其他材料，因此本研究为测试从相对简单（合金）到高度复杂（反铁磁储层）的不同材料体系开辟了可能性。