Human-AI collaboration for modeling heat conduction in nanostructures

TL;DR

This paper presents a human-AI collaborative framework for modeling heat conduction in nanostructures, combining AI-driven data generation with human interpretability to improve material design insights.

Contribution

It introduces a novel integrated human-AI collaboration approach that enhances physical understanding and data efficiency in modeling thermal properties of nanomaterials.

Findings

Achieved data-efficient modeling of thermal conductivity.

Generated interpretable parameters guiding nanostructure design.

Demonstrated the effectiveness of human-AI collaboration in materials modeling.

Abstract

In recent years, materials informatics, which combines data science and artificial intelligence (AI), has garnered significant attention owing to its ability to accelerate material development, reduce costs, and enhance product design. However, despite the widespread use of AI, human involvement is often limited to the initiation and oversight of machine learning processes and rarely includes more substantial roles that capitalize on human intuition or domain expertise. Consequently, true human-AI collaborations, where integrated insights can be maximized, are scarce. This study considers the problem of heat conduction in a two-dimensional nanostructure as a case study. An integrated human-AI collaboration framework is designed and used to construct a model to predict the thermal conductivity. This approach is used to determine the parameters that govern phonon transmission over the full range of frequencies and incidence angles. During operation, the self-learning entropic population annealing technique, which combines entropic sampling with a surrogate machine learning model, generates a small dataset that can be interpreted by a human. Therefore, data-efficient and global modeling is achieved, and parameters with physical interpretations are developed, which can guide nanostructural design to produce materials with specific properties. The proposed framework can leverage the complementary strengths of humans and AI, thereby enhancing the understanding and control of materials.