# Accelerated Discovery of Graphene Kirigami with an Enhanced Elastocaloric Effect via Machine Learning

## TL;DR

This paper uses machine learning to speed up the discovery of graphene kirigami designs with strong temperature-changing properties under stress.

## Contribution

A machine learning model, particularly a CNN, was developed to optimize graphene kirigami for enhanced elastocaloric performance.

## Key findings

- A CNN model achieved high accuracy in predicting the elastocaloric coefficient (RMSE = 0.064 K GPa–1; R² = 0.96).
- ML-guided optimization found high-ECC designs 10 times faster than random search.
- 16,807 GK configurations were evaluated to train and test the models.

## Abstract

Recent studies have
examined the elastocaloric response of graphene
kirigami (GK) and shown how it may be tailored through geometric design.
This tunability makes GK a promising platform for applications in
nanoscale solid-state thermal devices. In this work, we combine molecular
dynamics (MD) simulations and machine learning (ML) to explore how
GK geometries affect the elastocaloric coefficient (ECC), defined
as the adiabatic ratio between temperature change and applied tensile
stress. A data set of 16,807 GK configurations was generated through
systematic cut patterns and evaluated via MD at room temperature.
Using this data, both classical and deep-learning models were trained,
with a convolutional neural network (CNN) achieving the best performance
(RMSE = 0.064 K GPa–1; R
2 = 0.96). Model-guided optimization identified high-ECC designs 10
times faster than random search, demonstrating the power of ML-assisted
strategies for the accelerated discovery of advanced elastocaloric
materials.