"AI for science" is widely recognized as a future trend in the development of scientific research. Currently, although machine learning algorithms have played a crucial role in scientific research with numerous successful cases, relatively few instances exist where AI assists researchers in uncovering the underlying physical mechanisms behind a certain phenomenon and subsequently using that mechanism to improve machine learning algorithms' efficiency. This article uses the investigation into the relationship between extreme Poisson's ratio values and the structure of amorphous networks as a case study to illustrate how machine learning methods can assist in revealing underlying physical mechanisms. Upon recognizing that the Poisson's ratio relies on the low-frequency vibrational modes of dynamical matrix, we can then employ a convolutional neural network, trained on the dynamical matrix instead of traditional image recognition, to predict the Poisson's ratio of amorphous networks with a much higher efficiency. Through this example, we aim to showcase the role that artificial intelligence can play in revealing fundamental physical mechanisms, which subsequently improves the machine learning algorithms significantly.

翻译："人工智能驱动的科学"被普遍视为科学研究发展的未来趋势。当前，尽管机器学习算法已在科学研究中展现出重要作用并取得了大量成功案例，但借助AI协助研究人员揭示特定现象背后的物理机制、并利用该机制提升机器学习算法效率的实例仍相对较少。本文以极端泊松比值与非晶网络结构的关系研究为案例，阐述机器学习方法如何助力揭示潜在物理机制。在认识到泊松比依赖于动力学矩阵的低频振动模式后，我们可采用以动力学矩阵（而非传统图像识别）为训练对象的卷积神经网络，以更高效率预测非晶网络的泊松比。通过这一案例，我们旨在展示人工智能在揭示基本物理机制方面所发挥的作用，进而显著提升机器学习算法的性能。