Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models

TL;DR

This paper reviews how machine-learning models are transforming the design and discovery of functional nanomaterials by enabling faster, more accurate predictions and insights into nanoarchitecture and properties.

Contribution

It provides a comprehensive overview of the workflow for designing nanomaterials using machine-learning, highlighting recent advances, challenges, and future opportunities.

Findings

ML accelerates nanomaterials discovery and design.

Effective input-output linkage is crucial for model accuracy.

Challenges include data quality and model interpretability.

Abstract

Machine-learning (ML) techniques have revolutionized a host of research fields of chemical and materials science with accelerated, high-efficiency discoveries in design, synthesis, manufacturing, characterization and application of novel functional materials, especially at the nanometre scale. The reason is the time efficiency, prediction accuracy and good generalization abilities, which gradually replaces the traditional experimental or computational work. With enormous potentiality to tackle more real-world problems, ML provides a more comprehensive insight into combinations with molecules/materials under the fundamental procedures for constructing ML models, like predicting properties or functionalities from given parameters, nanoarchitecture design and generating specific models for other purposes. The key to the advances in nanomaterials discovery is how input fingerprints and output values can be linked quantitatively. Finally, some great opportunities and technical challenges are concluded in this fantastic field.