自动化扫描探针显微镜平台实现材料高通量发现，揭示铁电性能与成分的深层关联。
Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries
- 构建全自动SPM系统，结合多工具表征实现高通量材料探索
- 在SmBFO中发现双峰结构，在Al,Sc,B)N中定位铁电相变边界
- 用贝叶斯优化自主搜索，加速多目标物理规律发现
组合材料库为映射多组分相图中二元和三元截面物性演化提供了强大平台。尽管自20世纪60年代以来合成技术已取得进展，并通过实验室自动化进一步提速，其广泛应用仍依赖于对成分依赖性结构与功能的快速、定量测量。扫描探针显微术（SPM），包括压电力显微术（PFM），在提供功能性、空间分辨读数方面具有独特潜力。本文展示了一套完全自动化的SPM框架，用于探索二元Sm掺杂BiFeO3（SmBFO）和三元Al$_{1-x-y}$Sc$_x$B$_y$N（Al,Sc,B）N体系中的铁电性质。在SmBFO中，自动化探索识别出已知的准各向同性相边界并表现出增强的铁电响应，同时揭示了此前未报道的双峰精细结构。在（Al,Sc,B）N库中，铁电行为出现在相稳定性边界，与形貌和缺陷浓度变化相关。通过整合自动化SPM与波长色散谱（WDS）和光致发光成像，我们解析了成分-形貌-缺陷-性能之间的关系，并展示了迈向多工具、高通量表征平台的路径。最后，采用基于高斯过程的单目标和多目标贝叶斯优化，实现自主探索，凸显帕累托前沿作为平衡竞争物理收益的强大框架，加速数据驱动的物理发现。
原文摘要 · Abstract (English)
Combinatorial materials libraries provide a powerful platform for mapping how physical properties evolve across binary and ternary cross-sections of multicomponent phase diagrams. While synthesis of such libraries has advanced since the 1960s and been accelerated by laboratory automation, their broader utility depends on rapid, quantitative measurements of composition-dependent structures and functionalities. Scanning probe microscopies (SPM), including piezoresponse force microscopy (PFM), offer unique potential for providing these functionally relevant, spatially resolved readouts. Here, we demonstrate a fully automated SPM framework for exploring ferroelectric properties across combinatorial libraries, focusing on binary Sm-doped BiFeO3 (SmBFO) and ternary Al$_{1-x-y}$Sc$_x$B$_y$N (Al,Sc,B)N systems. In SmBFO, automated exploration identifies the known morphotropic phase boundary with enhanced ferroelectric response and reveals a previously unreported double-peak fine structure. In the (Al,Sc,B)N library, ferroelectric behavior emerges at the phase-stability boundary, correlating with variations in morphology and defect concentration. By integrating automated SPM with wavelength-dispersive spectroscopy (WDS) and photoluminescence mapping, we resolve the composition-morphology-defect-property relationships underlying ferroelectric response and demonstrate a pathway toward a multi-tool, high-throughput characterization platform. Finally, we implement Gaussian-process-based single- and multi-objective Bayesian optimization to enable autonomous exploration, highlighting the Pareto front as a powerful framework for balancing competing physical rewards and accelerating data-driven physics discovery.
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