Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909909169209344 |
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| author | Liu, Yu Raghavan, Aditya Pratiush, Utkarsh Ziatdinov, Maxim Lee, Chih-Yu Pant, Rohit Takeuchi, Ichiro Hsieh, Pochun Suceava, Albert Dimitrov, Edgar Terrones, Mauricio Gopalan, Venkatraman Mercer, Ian Spurling, R. Jackson Maria, Jon-Paul Kalinin, Sergei V. |
| author_facet | Liu, Yu Raghavan, Aditya Pratiush, Utkarsh Ziatdinov, Maxim Lee, Chih-Yu Pant, Rohit Takeuchi, Ichiro Hsieh, Pochun Suceava, Albert Dimitrov, Edgar Terrones, Mauricio Gopalan, Venkatraman Mercer, Ian Spurling, R. Jackson Maria, Jon-Paul Kalinin, Sergei V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18067 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries Liu, Yu Raghavan, Aditya Pratiush, Utkarsh Ziatdinov, Maxim Lee, Chih-Yu Pant, Rohit Takeuchi, Ichiro Hsieh, Pochun Suceava, Albert Dimitrov, Edgar Terrones, Mauricio Gopalan, Venkatraman Mercer, Ian Spurling, R. Jackson Maria, Jon-Paul Kalinin, Sergei V. Materials Science Mesoscale and Nanoscale Physics Artificial Intelligence 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. |
| title | Automated Materials Discovery Platform Realized: Scanning Probe Microscopy of Combinatorial Libraries |
| topic | Materials Science Mesoscale and Nanoscale Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2412.18067 |