论文标题
通过人工智能绘制材料属性，为热绝缘体加速材料空间探索
Accelerating Materials-Space Exploration for Thermal Insulators by Mapping Materials Properties via Artificial Intelligence
论文作者
论文摘要
可靠的人工智能模型有可能加速具有各种应用的最佳特性的材料，包括超导性，催化和热电学。该领域的进步通常受到可用数据的稀缺和质量以及获取新数据所需的重大努力的阻碍。对于此类应用，迫切需要使用易于访问的材料属性来帮助指导材料探索材料探索的可靠替代模型。在这里，我们提出了一个通用，数据驱动的框架，该框架通过符号回归和灵敏度分析的组合提供了定量预测以及为所有数据集指导数据创建的定性规则。我们通过仅使用75个实验测量值生成晶格导热率的精确分析模型来证明框架的功能。通过从该模型中提取最有影响力的材料特性，我们就可以层次筛选732材料并找到80种超胰岛材料。
Reliable artificial-intelligence models have the potential to accelerate the discovery of materials with optimal properties for various applications, including superconductivity, catalysis, and thermoelectricity. Advancements in this field are often hindered by the scarcity and quality of available data and the significant effort required to acquire new data. For such applications, reliable surrogate models that help guide materials space exploration using easily accessible materials properties are urgently needed. Here, we present a general, data-driven framework that provides quantitative predictions as well as qualitative rules for steering data creation for all datasets via a combination of symbolic regression and sensitivity analysis. We demonstrate the power of the framework by generating an accurate analytic model for the lattice thermal conductivity using only 75 experimentally measured values. By extracting the most influential material properties from this model, we are then able to hierarchically screen 732 materials and find 80 ultra-insulating materials.
