Researchers at the Nanoscience Center and at the
Faculty of Information Technology at the University of Jyväskylä in
Finland have demonstrated that new distance-based machine learning
methods developed at the University of Jyväskylä are capable of
predicting structures and atomic dynamics of nanoparticles
reliably. The new methods are significantly faster than traditional
simulation methods used for nanoparticle research and will
facilitate more efficient explorations of particle-particle
reactions and particles’ functionality in their environment. The
study was published in a Special Issue devoted to machine learning
in the Journal of Physical Chemistry on May 15, 2020.
