Identifying Useful Nanocrystal Morphologies Using Advanced Sampling Techniques and Machine Learning

Le résumé fourni par la source

We applied dimensionality reduction, together with both supervised and unsupervised machine learning (ML), to classify the temperature-dependent equilibrium shapes of Ag nanoparticles containing 100–200 atoms in 1–2 nm size range. The Ag nanocrystal shapes were generated using parallel tempering molecular dynamics. Using ML techniques, we identified five unique particle shape classes with 14 underlying subclasses. We considered the ramifications of our results for catalysis by characterizing the strain and coordination of the surface atoms for different particle subclasses. These studies revealed that icosahedra and hybrid decahedra-icosahedra possess the widest variety of under-coordinated surface atoms and the highest strain. This work helps to forge the link between structure and function and identify processing strategies for achieving beneficial nanocrystal morphologies.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.