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Autonomous nanoparticle synthesis by design

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arxiv 2505.13571 v1 pith:ITWBUWD2 submitted 2025-05-19 cond-mat.mtrl-sci cond-mat.mes-hallcs.LG

classification cond-mat.mtrl-scicond-mat.mes-hallcs.LG
keywords synthesisatomicautonomousstructuresdesignmaterialsscatteringsimulated
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Bridging Structure and Activity in Nanocatalysts via Machine Learning and Global Structure Representations

    cond-mat.mes-hall 2025-09 conditional novelty 5.0 of 10

    A Gaussian process trained on pair distance distribution fingerprints ranks 52,318 platinum nanoparticle structures for the oxygen reduction reaction and retrieves most of the top-100 most active candidates from rough...

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