Integrated Computational and Experimental Structure Refinement for Nanoparticles
Source abstract
Abstract
Determining the three-dimensional (3D) atomic structure of nanoparticles is critical to identifying the structures controlling their properties. Here, we demonstrate an integrated genetic algorithm (GA) optimization tool that refines the 3D structure of a nanoparticle by matching forward modeling to experimental scanning transmission electron microscopy (STEM) data and simultaneously minimizing the particle energy. We use the tool to create a refined 3D structural model of an experimentally observed ∼6000 atom Au nanoparticle.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Electron Microscopy Techniques and Applications · Advanced Materials Characterization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Materials Science and Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
Funding
Basic Energy Sciences · Division of Materials Research
References (43)
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