一种介于能带映射和能带结构之间的机器学习方法
A machine learning route between band mapping and band structure
摘要：
电子能带结构（BS）和晶体结构是固态材料的两个互补标识符。虽然方便的仪器和重建算法，使大型的，经验的，晶体结构数据库可能，提取准粒子色散（密切相关的BS）从光电带映射数据是目前有限的可用的计算方法。为了应对不断增长的光发射数据大小和规模，我们开发了一个管道，包括概率机器学习以及用于能带结构重建的相关数据处理、优化和评估方法，利用理论计算。该管道重建了半导体的所有14个价带，并在基准测试和其他材料数据集上显示出优异的性能。重建揭示了以前无法访问的动量空间结构信息的全球和局部规模，同时实现了与材料科学数据库集成的路径。我们的方法说明了机器学习和领域知识相结合的潜力，可扩展的特征提取多维数据。
英文摘要：
Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials. While convenient instruments and reconstruction algorithms have made large, empirical, crystal structure databases possible, extracting quasiparticle dispersion (closely related to BS) from photoemission band mapping data is currently limited by the available computational methods. To cope with the growing size and scale of photoemission data, we develop a pipeline including probabilistic machine learning and the associated data processing, optimization and evaluation methods for band structure reconstruction, leveraging theoretical calculations. The pipeline reconstructs all 14 valence bands of a semiconductor and shows excellent performance on benchmarks and other materials datasets. The reconstruction uncovers previously inaccessible momentum-space structural information on both global and local scales, while realizing a path towards integration with materials science databases. Our approach illustrates the potential of combining machine learning and domain knowledge for scalable feature extraction in multidimensional data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
