An Interpretable Machine Learning Model for Deformation of Multi-Walled Carbon Nanotubes

TL;DR

This paper introduces an interpretable machine learning model that accurately predicts complex deformation patterns in multi-walled carbon nanotubes, offering a faster alternative to atomistic-physics models with enhanced interpretability.

Contribution

It develops a novel nonlinear dimensionality reduction technique combined with deep learning to efficiently model nanotube deformations with interpretability.

Findings

Model matches atomistic-physics accuracy

Significantly faster computation

Extracts universal deformation patterns

Abstract

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.