通过不确定性感知多保真度筛选加速具有极端功函数的材料发现
Accelerated Discovery of Materials with Extreme Work Functions through Uncertainty-Aware Multi-Fidelity Screening
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中文总结 AI 辅助
本研究结合机器学习与多保真度筛选，开发数据驱动框架，筛选550万种化合物，识别出低、高功函数候选材料，为相关应用加速极端功函数材料发现。
中文摘要 AI 辅助
功函数在能量转换、电子学到催化等诸多技术领域中发挥着关键作用。本研究将机器学习（ML）与多保真度筛选相结合，开发了一种数据驱动框架以加速具有极端功函数的材料发现。我们对已发表的功函数随机森林（RF）模型进行了扩充，纳入了预测不确定性校准和适用域评估，以提升预测鲁棒性。通过将扩充后的RF模型与通用机器学习原子间势模拟及针对性的从头算计算相结合，我们从GNoME和Alexandria数据库中筛选了550万种化合物。该工作流程共识别出209个功函数低于2.0 eV的极低功函数表面，以及227个功函数高于6.0 eV的极高功函数表面，分别对应136种和172种独特材料。所得候选材料揭示了与既定化学原理一致的趋势，包括碱金属和碱土金属终止表面倾向于呈现低功函数；同时也发现了非传统基序：富含镧系元素的表面终止与极低功函数密切相关，而顶层含有类金属或磷的表面则与极高功函数相关。本研究展示了一种可扩展策略，该策略利用ML模型和多保真度计算工作，为先进电子、能量转换及催化应用加速具有极端功函数的材料发现。
英文摘要
Work function plays a pivotal role in technologies ranging from energy conversion and electronics to catalysis. In this work, we integrated machine learning (ML) with multi-fidelity screening to develop a data-driven framework for accelerating the discovery of materials with extreme work functions. We augmented a previously published Random Forest (RF) model for work function to include prediction uncertainty calibration and domain of applicability assessment to enhance prediction robustness. By combining the augmented RF model with universal ML interatomic potential simulations and targeted ab initio calculations, we screened 5.5 million compounds from the GNoME and Alexandria databases. This workflow identified 209 surfaces with extreme low work functions below 2.0 eV and 227 surfaces with extreme high work functions above 6.0 eV, corresponding to 136 and 172 unique materials, respectively. The resulting candidates revealed trends consistent with established chemical principles, including the tendency of alkali- and alkaline-earth-terminated surfaces to exhibit low work functions. While it also uncovered less conventional motifs: lanthanide-rich surface terminations were strongly associated with extremely low work functions, whereas surfaces containing metalloids or phosphorus at the top layer were correlated with exceptionally high work functions. This work demonstrates a scalable strategy that leverages ML models and multi-fidelity computational efforts to accelerate the discovery of materials with extreme work functions for advanced electronic, energy-conversion, and catalytic applications.
