arXiv 2309.033982023-09-06cond-mat.mtrl-sci

具有增强压电响应的纤锌矿固溶体的自动化发现

Automated Discovery of Wurtzite Solid Solutions with Enhanced Piezoelectric Response

Drew Behrendt, Sayan Banerjee, Jiahao Zhang, Andrew M. Rappe

AI总结：

本研究结合机器学习、化学直觉和热力学的多级筛选协议，在9种纤锌矿基础体系中探索3000多种三元固溶体，预测出11种因掺入电正性掺杂剂而增强压电响应的材料。

摘要：

尽管已知许多压电材料，但通过组分掺杂形成固溶体来改善现有材料的品质因数仍有巨大潜力。具体而言，已证明对纤锌矿结构材料进行掺杂和合金化可改善压电响应；然而，广阔的组分空间仍有待探索。在本工作中，我们应用结合机器学习、化学直觉和热力学的多级筛选协议，系统地发现纤锌矿材料空间中能改善所需压电响应的掺杂剂组合。通过该协议，我们使用计算成本低廉的筛选计算，考虑了来自9种不同纤锌矿基础体系（AlN、BeO、CdS、CdSe、GaN、ZnO、ZnS、ZnSe和AgI）的3000多种可能的三元纤锌矿固溶体。最后，基于热力学分析和显式压电响应计算，我们预测了11种因掺入电正性掺杂剂而具有增强压电响应的材料。

英文摘要：

While many piezoelectric materials are known, there is still great potential to improve on the figures of merit of existing materials through compositional doping, forming solid solutions. Specifically, it has been shown that doping and alloying wurtzite-structured materials can improve the piezoelectric response; however, a vast compositional space has remained unexplored. In this work, we apply a multi-level screening protocol combining machine learning, chemical intuition, and thermodynamics to systematically discover dopant combinations in wurtzite material space that improve the desired piezoelectric response. Through our protocol, we use computationally inexpensive screening calculations to consider more than 3000 possible ternary wurtzite solid solutions from 9 different wurtzite base systems: AlN, BeO, CdS, CdSe, GaN, ZnO, ZnS, ZnSe, and AgI. Finally, based on thermodynamic analysis and explicit piezoelectric response calculations, we predict 11 materials with improved piezoelectric response, due to the incorporation of electropositive dopants.

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