论文标题
多壁碳纳米管变形的可解释的机器学习模型
An Interpretable Machine Learning Model for Deformation of Multi-Walled Carbon Nanotubes
论文作者
论文摘要
我们提出了一种新型的可解释的机器学习模型，以准确预测由数百万原子制成的多壁碳纳米管（MWCNTS）的复杂波纹变形。基于原子物理的模型是准确的，但对于如此大的系统，基于计算的模型。为了克服这种瓶颈，我们开发了一种机器学习模型，该模型由一种新颖的维度降低技术和缩小维度中的深层神经网络学习组成。提出的非线性降低降低技术扩展了功能主成分分析，以满足变形的限制。它的新颖性在于设计一个恰好满足约束的功能空间，这对于有效的维度降低至关重要。由于降低维度和本工作中采用的其他几种策略，通过深层神经网络的学习非常准确。提出的模型可以准确地匹配基于原子物理的模型，同时更快地列出了数量级。它以无监督的方式提取了普遍主导的变形模式。这些模式是可理解的，并阐明了该模型的预测方式，从而产生了解释性。提出的模型可以为探索机器学习朝向一维材料的力学的基础。
We present a novel interpretable machine learning model to accurately predict complex rippling deformations of Multi-Walled Carbon Nanotubes(MWCNTs) made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model that consists of a novel dimensionality reduction technique and a deep neural network-based learning in the reduced dimension. The proposed nonlinear dimensionality reduction technique extends the functional principal component analysis to satisfy the constraint of deformation. Its novelty lies in designing a function space that satisfies the constraint exactly, which is crucial for efficient dimensionality reduction. Owing to the dimensionality reduction and several other strategies adopted in the present work, learning through deep neural networks is remarkably accurate. The proposed model accurately matches an atomistic-physics-based model while being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and elucidate how the model predicts, yielding interpretability. The proposed model can form a basis for the exploration of machine learning toward the mechanics of one and two-dimensional materials.
