Machine learning of microscopic ingredients for graphene oxide/cellulose interaction

TL;DR

This study combines first-principles calculations and machine learning to identify microscopic features influencing graphene oxide's binding strength to cellulose, enabling better control of nanocomposite interactions.

Contribution

It introduces a machine learning approach to classify and predict binding energies based on microscopic attributes, advancing nanocomposite design.

Findings

Successful classification of systems into high and low binding energy groups.

Identification of key features from X-ray photoelectron spectroscopy.

Demonstration of regression models predicting binding energies from microscopic parameters.

Abstract

Understanding the role of microscopic attributes in nanocomposites allows for a controlled and, therefore, acceleration in experimental system designs. In this work, we extracted the relevant parameters controlling the graphene oxide binding strength to cellulose by combining first-principles calculations and machine learning algorithms. We were able to classify the systems among two classes with higher and lower binding energies, which are well defined based on the isolated graphene oxide features. By a theoretical X-ray photoelectron spectroscopy analysis, we show the extraction of these relevant features. Additionally, we demonstrate the possibilities of a refined control within a machine learning regression between the binding energy values and the system's characteristics. Our work presents a guiding map to the control graphene oxide/cellulose interaction.