Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy
Abstract Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01642-1 |
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| author | Ganesh Narasimha Dejia Kong Paras Regmi Rongying Jin Zheng Gai Rama Vasudevan Maxim Ziatdinov |
| author_facet | Ganesh Narasimha Dejia Kong Paras Regmi Rongying Jin Zheng Gai Rama Vasudevan Maxim Ziatdinov |
| author_sort | Ganesh Narasimha |
| collection | DOAJ |
| description | Abstract Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn2As2, a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects. |
| format | Article |
| id | doaj-art-04a72b0d13d94287b1028d95d70e3c1e |
| institution | OA Journals |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-04a72b0d13d94287b1028d95d70e3c1e2025-08-20T02:10:31ZengNature Portfolionpj Computational Materials2057-39602025-06-0111111010.1038/s41524-025-01642-1Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopyGanesh Narasimha0Dejia Kong1Paras Regmi2Rongying Jin3Zheng Gai4Rama Vasudevan5Maxim Ziatdinov6Center for Nanophase Material Sciences (CNMS), Oak Ridge National Laboratory (ORNL)Center for Nanophase Material Sciences (CNMS), Oak Ridge National Laboratory (ORNL)SmartState Center for Experimental Nanoscale Physics, Department of Physics and Astronomy, University of South CarolinaSmartState Center for Experimental Nanoscale Physics, Department of Physics and Astronomy, University of South CarolinaCenter for Nanophase Material Sciences (CNMS), Oak Ridge National Laboratory (ORNL)Center for Nanophase Material Sciences (CNMS), Oak Ridge National Laboratory (ORNL)Center for Nanophase Material Sciences (CNMS), Oak Ridge National Laboratory (ORNL)Abstract Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn2As2, a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.https://doi.org/10.1038/s41524-025-01642-1 |
| spellingShingle | Ganesh Narasimha Dejia Kong Paras Regmi Rongying Jin Zheng Gai Rama Vasudevan Maxim Ziatdinov Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy npj Computational Materials |
| title | Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy |
| title_full | Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy |
| title_fullStr | Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy |
| title_full_unstemmed | Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy |
| title_short | Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy |
| title_sort | uncovering multiscale structure property correlations via active learning in scanning tunneling microscopy |
| url | https://doi.org/10.1038/s41524-025-01642-1 |
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