近年來，被稱為科學研究正規化“第四次工業革命”的大資料與人工智慧的結合引起了研究者們強烈的關注。其中，作為核心技術之一的機器學習已被成功地應用於物理學、材料科學、生物醫藥、影象識別等諸多領域。特別地，機器學習在新能源材料的探索上也在以驚人的速度發展。其中，對於高效能熱電材料的快速搜尋尤為引人關注。在與熱電效能密切相關的輸運係數中，載流子弛豫時間的預測一直以來都是一個十分重要但又較為複雜的基礎科學問題。雖然人們可以使用簡單的形變勢理論或是全面考慮電-聲耦合來預測弛豫時間，但這兩種方法都只能處理原胞較小的材料體系。

來自武漢大學物理科學與技術學院的劉惠軍教授團隊，採用一種名為SISSO（確定獨立篩選與稀疏運算子）的機器學習方法，提出了形式簡單並且物理意義明確的高通量描述符，快速有效地預測了超過1600萬個具有任意化學配比的輝碲鉍礦族化合物的載流子弛豫時間。這項研究工作對於高效能熱電材料的搜尋以及層狀拓撲材料熱電效能的最佳化，具有重要的指導意義。

該文近期發表於npj Computational Materials 6: 149 (2020)，英文標題與摘要如下，點選https://www.nature.com/articles/s41524-020-00417-0可以自由獲取論文PDF。

High-throughput prediction of the carrier relaxation time via data-driven descriptor

Zizhen Zhou, Guohua Cao, Jianghui Liu, Huijun Liu

It has been demonstrated that many promising thermoelectric materials such as tetradymite compounds are also three-dimensional topological insulators. In both cases, a fundamental question is the evaluation of carrier relaxation time, which is usually a rough task due to the complicated scattering mechanisms. Previous works using the simple deformation potential theory or considering complete electron-phonon coupling are however restricted to small systems. By adopting a data-driven method named SISSO (Sure Independence Screening and Sparsifying Operator) with the training data obtained via deformation potential theory, we propose an efficient and physically interpretable descriptor to evaluate the relaxation time, using tetradymites as prototypical examples. Without any input from first-principles calculations, the descriptor contains only several elemental properties of the constituent atoms, and could be utilized to quickly and reliably predict the carrier relaxation time of a substantial number of tetradymites with arbitrary stoichiometry.