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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.13571 |
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| _version_ | 1866909616850337792 |
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| author | Anker, Andy S. Jensen, Jonas H. Gonzalez-Duque, Miguel Moreno, Rodrigo Smolska, Aleksandra Juelsholt, Mikkel Hardion, Vincent Jorgensen, Mads R. V. Faina, Andres Quinson, Jonathan Stoy, Kasper Vegge, Tejs |
| author_facet | Anker, Andy S. Jensen, Jonas H. Gonzalez-Duque, Miguel Moreno, Rodrigo Smolska, Aleksandra Juelsholt, Mikkel Hardion, Vincent Jorgensen, Mads R. V. Faina, Andres Quinson, Jonathan Stoy, Kasper Vegge, Tejs |
| contents | Controlled synthesis of materials with specified atomic structures underpins technological advances yet remains reliant on iterative, trial-and-error approaches. Nanoparticles (NPs), whose atomic arrangement dictates their emergent properties, are particularly challenging to synthesise due to numerous tunable parameters. Here, we introduce an autonomous approach explicitly targeting synthesis of atomic-scale structures. Our method autonomously designs synthesis protocols by matching real time experimental total scattering (TS) and pair distribution function (PDF) data to simulated target patterns, without requiring prior synthesis knowledge. We demonstrate this capability at a synchrotron, successfully synthesising two structurally distinct gold NPs: 5 nm decahedral and 10 nm face-centred cubic structures. Ultimately, specifying a simulated target scattering pattern, thus representing a bespoke atomic structure, and obtaining both the synthesised material and its reproducible synthesis protocol on demand may revolutionise materials design. Thus, ScatterLab provides a generalisable blueprint for autonomous, atomic structure-targeted synthesis across diverse systems and applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13571 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autonomous nanoparticle synthesis by design Anker, Andy S. Jensen, Jonas H. Gonzalez-Duque, Miguel Moreno, Rodrigo Smolska, Aleksandra Juelsholt, Mikkel Hardion, Vincent Jorgensen, Mads R. V. Faina, Andres Quinson, Jonathan Stoy, Kasper Vegge, Tejs Materials Science Mesoscale and Nanoscale Physics Machine Learning Controlled synthesis of materials with specified atomic structures underpins technological advances yet remains reliant on iterative, trial-and-error approaches. Nanoparticles (NPs), whose atomic arrangement dictates their emergent properties, are particularly challenging to synthesise due to numerous tunable parameters. Here, we introduce an autonomous approach explicitly targeting synthesis of atomic-scale structures. Our method autonomously designs synthesis protocols by matching real time experimental total scattering (TS) and pair distribution function (PDF) data to simulated target patterns, without requiring prior synthesis knowledge. We demonstrate this capability at a synchrotron, successfully synthesising two structurally distinct gold NPs: 5 nm decahedral and 10 nm face-centred cubic structures. Ultimately, specifying a simulated target scattering pattern, thus representing a bespoke atomic structure, and obtaining both the synthesised material and its reproducible synthesis protocol on demand may revolutionise materials design. Thus, ScatterLab provides a generalisable blueprint for autonomous, atomic structure-targeted synthesis across diverse systems and applications. |
| title | Autonomous nanoparticle synthesis by design |
| topic | Materials Science Mesoscale and Nanoscale Physics Machine Learning |
| url | https://arxiv.org/abs/2505.13571 |