  Functional nanoparticles (NPs) have gained significant attention as a
promising application in various fields, including sensor, smart coating, drug
delivery, and more. Here, we propose a novel mechanism assisted by
machine-learning workflow to accurately predict phase diagram of NPs, which
elegantly achieves tunability of shapes and internal structures of NPs using
self-assembly of block-copolymers (BCP). Unlike most of previous studies, we
obtain onion-like and mesoporous NPs in neutral environment and hamburger-like
NPs in selective environment. Such novel phenomenon is obtained only by
tailoring the topology of a miktoarm star BCP chain architecture without the
need for any further treatment. Moreover, we demonstrate that the BCP chain
architecture can be used as a new strategy for tuning the lamellar asymmetry of
NPs. We show that the asymmetry between A and B lamellae in striped ellipsoidal
and onion-like particles increases as the volume fraction of the A-block
increases, beyond the level reached by linear BCPs. In addition, we find an
extended region of onion-like structure in the phase diagram of A-selective
environment, as well as the emergence of an inverse onion-like structure in the
B-selective one. Our findings provide a valuable insight into the design and
fabrication of nanoscale materials with customized properties, opening up new
possibilities for advanced applications in sensing, materials science, and
beyond.
