用分层强化的漏斗学习法，高效找出低热导率半导体材料。
Hierarchy-Boosted Funnel Learning for Identifying Semiconductors with Ultralow Lattice Thermal Conductivity
- 先用无监督学习筛选候选，再聚焦少量样本训练有监督模型。
- 仅用数百个数据点就精准预测出超低晶格热导率材料。
- 适合材料发现、热电应用研究者，可加速新材料设计。
数据驱动的机器学习在材料性质预测中展现出巨大潜力。然而，在广阔的化学空间中，昂贵性质标签的数据稀缺，给机器学习高效预测性质并揭示结构-性质关系带来挑战。本文提出一种新型分层强化漏斗学习（HiBoFL）框架，成功应用于识别具有超低晶格热导率（κₗ）的半导体材料。通过在数万种材料中由无监督学习筛选出的数百种目标材料上训练，实现了对超低κₗ的高效且可解释的有监督预测，从而避免了缺乏明确目标的大规模第一性原理计算。结果提供了一组潜在热电应用的超低κₗ候选材料，并发现了一个显著影响结构非谐性的新因素。该HiBoFL框架为加速功能材料发现提供了新路径。
原文摘要 · Abstract (English)
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 ($κ_\mathrm{L}$). 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 $κ_\mathrm{L}$, thereby circumventing large-scale brute-force \textit{ab initio} calculations without clear objectives. As a result, we provide a list of candidates with ultralow $κ_\mathrm{L}$ 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.
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