Autonomous Synthesis of Nanoparticles with Target Scattering Patterns

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,<sup>1-5</sup> are particularly challenging to synthesize due to numerous tunable parameters. Here, we introduce an autonomous approach that explicitly targets atomic-scale structure through scattering patterns. 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 embedded synthesis knowledge. We demonstrate this capability at a synchrotron by targeting two structurally distinct gold NP scattering patterns: 5 nm decahedral and 10 nm face-centered cubic structures. Ultimately, specifying target scattering patterns and autonomously approaching synthesis protocols that reproduce them experimentally may enable on-demand, atomic structure-informed materials design. ScatterLab thus provides a generalizable blueprint for autonomous, atomic structure-targeted synthesis across diverse systems and applications.

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Publication Details

Journal
ACS Nano
Published
2026-02-18
DOI
https://doi.org/10.1021/acsnano.5c15488
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Autonomous Synthesis of Nanoparticles with Target Scattering Patterns

Jonas Haugaard Jensen, Aleksandra Smolska, Andrés Faíña, Andy S. Anker et al.
ACS Nano
Machine Learning in Materials Science
article

Autonomous Synthesis of Nanoparticles with Target Scattering Patterns

Jonas Haugaard Jensen, Aleksandra Smolska, Andrés Faíña, Andy S. Anker, Jonathan Quinson, Mikkel Juelsholt, Vincent Hardion, Rodrigo Moreno, Tejs Vegge, Kasper Støy, Mads R. V. Jørgensen
article en

Abstract

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,<sup>1-5</sup> are particularly challenging to synthesize due to numerous tunable parameters. Here, we introduce an autonomous approach that explicitly targets atomic-scale structure through scattering patterns. 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 embedded synthesis knowledge. We demonstrate this capability at a synchrotron by targeting two structurally distinct gold NP scattering patterns: 5 nm decahedral and 10 nm face-centered cubic structures. Ultimately, specifying target scattering patterns and autonomously approaching synthesis protocols that reproduce them experimentally may enable on-demand, atomic structure-informed materials design. ScatterLab thus provides a generalizable blueprint for autonomous, atomic structure-targeted synthesis across diverse systems and applications.

ACS NanoVol. 20(8)
Universidade da Coruña (ES), University of Copenhagen (DK), Lund University (SE), Aarhus University (DK), University of Oxford (GB), Biological E (India) (IN), MAX IV Laboratory (SE), Columbia University (US), IT University of Copenhagen (DK), Technical University of Denmark (DK)
Openalex Percentile: Top 53%
Machine Learning in Materials Science
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