A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networks

TL;DR

This paper demonstrates how AI can uncover physical mechanisms, specifically linking vibrational modes to Poisson's ratio in amorphous networks, and use this understanding to enhance machine learning efficiency.

Contribution

It introduces a novel approach where machine learning reveals underlying physical mechanisms, leading to improved algorithm performance in material property prediction.

Findings

CNN trained on dynamical matrices predicts Poisson's ratio efficiently

Physical mechanism linking vibrational modes to Poisson's ratio identified

AI-assisted discovery enhances machine learning in scientific research

Abstract

"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.