Open AccessPrediction of Nanoparticle Sizes for Arbitrary Methacrylates Using Artificial Neuronal Networks
Author(s) -
Kimmig Julian,
Schuett Timo,
Vollrath Antje,
Zechel Stefan,
Schubert Ulrich S.
Publication year - 2021
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202102429
Subject(s) - nanoparticle , generalizability theory , methacrylate , materials science , polymer , computer science , graph , particle size , biological system , polymerization , nanotechnology , theoretical computer science , chemical engineering , mathematics , composite material , engineering , statistics , biology
Particle sizes represent one of the key factors influencing the usability and specific targeting of nanoparticles in medical applications such as vectors for drug or gene therapy. A multi‐layered graph convolutional network combined with a fully connected neuronal network is presented for the prediction of the size of nanoparticles based only on the polymer structure, the degree of polymerization, and the formulation parameters. The model is capable of predicting particle sizes obtained by nanoprecipitation of different poly(methacrylates). This includes polymers the network has not been trained with, indicating the high potential for generalizability of the model. By utilizing this model, a significant amount of time and resources can be saved in formulation optimization without extensive primary testing of material properties.