Chris Wolverton

Yifan Sun, Zhi Li, Tetsuya Imamura et al.

Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, $ZT$. To accelerate the discovery of high-$ZT$ materials, efforts have focused on identifying compounds with low thermal conductivity $κ$. Using a curated dataset of 71,913 entries, we show that high-$ZT$ materials reside not only in the low-$κ$ regime but also cluster near a lattice-to-total thermal conductivity ratio ($κ_\mathrm{L}/κ$) of approximately 0.5, consistent with the phonon-glass electron-crystal design concept. Building on this insight, we construct a framework consisting of two machine learning models for the lattice and electronic components of thermal conductivity that jointly provide both $κ$ and $κ_\mathrm{L}/κ$ for screening and guiding the optimization of TE materials. Among 104,567 compounds screened, our models identify 2,522 ultralow-$κ$ candidates. Follow-up case studies demonstrate that this framework can reliably provide optimization strategies by suggesting new dopants and alloys that shift pristine materials toward the $κ_\mathrm{L}/κ$ approaching 0.5 regime. Ultimately, by integrating rapid screening with PGEC-guided optimization, our data-driven framework effectively bridges the critical gap between materials discovery and performance enhancement.

Zhenhao Zhou, Salman Bin Kashif, Jin-Hu Dou et al.

Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design. Machine learning offers a compelling pathway to accelerate the exploration, but existing models lack either interpretability or fidelity for elucidating the correlation between crystal geometry and property. Here, we report a three-dimensional periodic space sampling method that decomposes large nanoporous structures into local geometrical sites for combined property prediction and site-wise contribution quantification. Trained with a constructed database and retrieved datasets, our model achieves state-of-the-art accuracy and data efficiency for property prediction on gas storage, separation, and electrical conduction. Meanwhile, this approach enables the interpretation of the prediction and allows for accurate identification of significant local sites for targeted properties. Through identifying transferable high-performance sites across diverse nanoporous frameworks, our model paves the way for interpretable, symmetry-aware nanoporous materials design, which is extensible to other materials, like molecular crystals and beyond.