Recent years have seen a drastic increase in the scientific use of machine learning (ML) techniques, yet their applications remain limited for many fields. Here, we demonstrate techniques that allow overcoming two obstacles to the widespread adoption of ML, particularly relevant to nanomaterials and nanoscience fields. Using the prediction of the band gap values of carbon nanotubes as a typical example, we address the representation of the periodic data as well as training on extremely small datasets. We successfully showed that careful choice of the activation function allows capturing periodic tendencies in the datasets that are common in physical data and previously posed significant difficulty for neural networks. In particular, utilization of the recently proposed parametric periodic Snake activation function shows a dramatic improvement. Furthermore, tackling a typical lack of accurate data, we used the transfer learning technique utilizing more abundant low-quality computational data and achieving outstanding accuracy on a significantly expanded dataspace. This strategy was enabled by the use of a combination of the Snake and ReLU layers, capturing data periodicity and amplitude, respectively. Hence, retraining only ReLU layers allowed the transfer of the periodic tendencies captured from low-quality data to the final high-accuracy neural network. Those techniques are expected to expand the usability of ML approaches in application to physical data in general and the fields of nanomaterials in particular.

近年来，机器学习 (ML) 技术的科学应用急剧增加，但其应用在许多领域仍然受到限制。在这里，我们展示了能够克服机器学习广泛采用的两个障碍的技术，特别是与纳米材料和纳米科学领域相关的技术。以碳纳米管带隙值的预测作为典型示例，我们解决了周期性数据的表示以及极小数据集的训练。我们成功地表明，仔细选择激活函数可以捕获数据集中的周期性趋势，这些趋势在物理数据中很常见，并且之前给神经网络带来了很大的困难。特别是，最近提出的参数化周期性 Snake 激活函数的利用显示出显着的改进。此外，为了解决准确数据的典型缺乏问题，我们使用了迁移学习技术，利用更丰富的低质量计算数据，并在显着扩展的数据空间上实现了出色的准确性。该策略是通过使用 Snake 层和 ReLU 层的组合来实现的，分别捕获数据周期性和幅度。因此，仅重新训练 ReLU 层可以将从低质量数据捕获的周期性趋势转移到最终的高精度神经网络。这些技术有望扩展机器学习方法在一般物理数据、特别是纳米材料领域的应用的可用性。