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.
Authors
- Jonas Haugaard Jensen
- Aleksandra Smolska (ORCID: https://orcid.org/0000-0002-1145-2462)
- Andrés Faíña (ORCID: https://orcid.org/0000-0001-7288-6988)
- Andy S. Anker (ORCID: https://orcid.org/0000-0002-7403-6642)
- Jonathan Quinson (ORCID: https://orcid.org/0000-0002-9374-9330)
- Mikkel Juelsholt (ORCID: https://orcid.org/0000-0001-6401-8267)
- Vincent Hardion
- Rodrigo Moreno
- Tejs Vegge
- Kasper Støy
- Mads R. V. Jørgensen
Institutions
- 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)
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
- 0.00