Energetically Driven Structure Matching for Autonomous Total X-ray Scattering Experiments
Abstract
The emergence of autonomous laboratories motivates rapid conversion of experimental data into reliable atomistic models on time-scales compatible with closed-loop optimization. Here we develop an energetically driven structure matching framework for analysis during ongoing total X-ray scattering experiments. Using data from gold nanoparticles, we match against idealized spherical, octahedral, decahedral, and icosahedral geometries, their machine-learned interatomic potential (MLIP)-relaxed structures, and molecular dynamics (MD) ensembles. Idealized models are fast to generate but can misassign morphology and systematically underestimate size by neglecting surface relaxation, strain, and thermal disorder. MLIP relaxation markedly improves both, while MD ensemble averaging agrees best with experiment. We therefore introduce a hierarchical workflow combining rapid idealized screening with targeted MLIP and MD refinement of top candidates, delivering improved structural feedback without interrupting autonomous operation. This framework provides a route towards autonomous campaigns in which the target structure itself can be updated in response to the evolving energy landscape of structures compatible with the experimental data.
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BibTeX
@misc{frost2026energetically,
title = {{Energetically Driven Structure Matching for Autonomous Total X-ray Scattering Experiments}},
author = {Emil J. P. Frost and Martin A. Karlsen and Jonas H. Jensen and Rodrigo Moreno and Jørn Lambertsen and Jonathan Quinson and Andy S. Anker and Alexander Bagger and Tejs Vegge},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30852},
eprint = {2609.30852},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30852},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Energetically Driven Structure Matching for Autonomous Total X-ray Scattering Experiments
AU - Emil J. P. Frost
AU - Martin A. Karlsen
AU - Jonas H. Jensen
AU - Rodrigo Moreno
AU - Jørn Lambertsen
AU - Jonathan Quinson
AU - Andy S. Anker
AU - Alexander Bagger
AU - Tejs Vegge
PY - 2026
DA - 2026/09/25
PB - arXiv
DO - 10.48550/arxiv.2609.30852
UR - https://arxiv.org/abs/2609.30852
AB - The emergence of autonomous laboratories motivates rapid conversion of experimental data into reliable atomistic models on time-scales compatible with closed-loop optimization. Here we develop an energetically driven structure matching framework for analysis during ongoing total X-ray scattering experiments. Using data from gold nanoparticles, we match against idealized spherical, octahedral, decahedral, and icosahedral geometries, their machine-learned interatomic potential (MLIP)-relaxed structures, and molecular dynamics (MD) ensembles. Idealized models are fast to generate but can misassign morphology and systematically underestimate size by neglecting surface relaxation, strain, and thermal disorder. MLIP relaxation markedly improves both, while MD ensemble averaging agrees best with experiment. We therefore introduce a hierarchical workflow combining rapid idealized screening with targeted MLIP and MD refinement of top candidates, delivering improved structural feedback without interrupting autonomous operation. This framework provides a route towards autonomous campaigns in which the target structure itself can be updated in response to the evolving energy landscape of structures compatible with the experimental data.
ER -