用于建议意外热电化学的推荐引擎
A recommendation engine for suggesting unexpected thermoelectric chemistries
摘要：
对新热电材料的实验研究仍然主要局限于有限的一组成功的化学和结构家族，如硫属化物，方钴矿和Zintl相。原则上，计算工具，如密度泛函理论（DFT）提供了合理地指导实验合成工作的可能性，对非常不同的化学。然而，在实践中，从第一原理预测热电性能仍然是一项具有挑战性的工作，实验研究人员通常不会直接使用计算来驱动自己的合成工作。为了弥合实验需求和计算工具之间的实际差距，我们报告了一个开放的基于机器学习的推荐引擎（thermoelectrics.citrination.com），为材料研究人员提供有前途的新热电组合物，并评估用户设计的化合物的可行性。我们表明，该引擎可以识别有趣的化学非常不同的已知热电。具体来说，我们描述了一个示例性的一组化合物的实验表征，从我们的发动机，RE12Co5Bi（RE = Gd，Er），表现出令人惊讶的热电性能，由于其前所未有的高负载与金属d和f块元素，并保证进一步调查作为一个新的热电材料平台。
英文摘要：
The experimental search for new thermoelectric materials remains largely confined to a limited set of successful chemical and structural families, such as chalcogenides, skutterudites, and Zintl phases. In principle, computational tools such as density functional theory (DFT) offer the possibility of rationally guiding experimental synthesis efforts toward very different chemistries. However, in practice, predicting thermoelectric properties from first principles remains a challenging endeavor, and experimental researchers generally do not directly use computation to drive their own synthesis efforts. To bridge this practical gap between experimental needs and computational tools, we report an open machine learning-based recommendation engine (http://thermoelectrics.citrination.com) for materials researchers that suggests promising new thermoelectric compositions, and evaluates the feasibility of user-designed compounds. We show that this engine can identify interesting chemistries very different from known thermoelectrics. Specifically, we describe the experimental characterization of one example set of compounds derived from our engine, RE12Co5Bi (RE = Gd, Er), which exhibits surprising thermoelectric performance given its unprecedentedly high loading with metallic d and f block elements, and warrants further investigation as a new thermoelectric material platform.
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