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arXiv · 2412.18067

Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries

Yu Liu·Aditya Raghavan·Utkarsh Pratiush·Maxim Ziatdinov·Chih-Yu Lee·Rohit Pant·Ichiro Takeuchi·Pochun Hsieh·Albert Suceava·Edgar Dimitrov·Mauricio Terrones·Venkatraman Gopalan·Ian Mercer·R. Jackson Spurling·Jon-Paul Maria·Sergei V. Kalinin
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Abstract

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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Yu Liu, Aditya Raghavan, Utkarsh Pratiush, Maxim Ziatdinov, Chih-Yu Lee, Rohit Pant, Ichiro Takeuchi, Pochun Hsieh, Albert Suceava, Edgar Dimitrov, Mauricio Terrones, Venkatraman Gopalan, Ian Mercer, R. Jackson Spurling, Jon-Paul Maria, Sergei V. Kalinin. 2024-12-24. Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries. https://arxiv.org/abs/2412.18067

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