Hierarchy-Boosted Funnel Learning for Identifying Semiconductors with Ultralow Lattice Thermal Conductivity

TL;DR

This paper introduces HiBoFL, a hierarchical machine learning framework that efficiently predicts ultralow lattice thermal conductivity in semiconductors using limited data, aiding rapid materials discovery.

Contribution

The paper presents a novel hierarchy-boosted funnel learning framework that enables accurate property prediction with minimal labeled data, bypassing extensive ab initio calculations.

Findings

Identified new semiconductor candidates with ultralow thermal conductivity.

Discovered a new factor influencing structural anharmonicity.

Achieved interpretable predictions with limited training data.

Abstract

Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships. Here, we propose a novel hierarchy-boosted funnel learning (HiBoFL) framework, which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity ($\kappa_\mathrm{L}$). By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands, we achieve efficient and interpretable supervised predictions of ultralow $\kappa_\mathrm{L}$, thereby circumventing large-scale brute-force \textit{ab initio} calculations without clear objectives. As a result, we provide a list of candidates with ultralow $\kappa_\mathrm{L}$ for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity. This HiBoFL framework offers a novel practical pathway for accelerating the discovery of functional materials.