Abstract
Current study results on Machine Learning have been published. According to news reporting from Paris, France, by NewsRx journalists, research stated, "We investigate the microscopic mechanism of the thermally induced ambient pressure ordered-disordered phase transitions of two zeolitic imidazolate frameworks of formula Zn(C3H3N2)(2): a porous (ZIF-4) and a dense, non-porous (ZIF-zni) polymorph via a combination of data science and computer simulation approaches. Molecular dynamics simulations are carried out at the atomistic level through the nb-ZIF-FF force field that incorporates ligand-metal reactivity and relies on dummy atoms to reproduce the correct tetrahedral topology around Zn2+ centres." Financial support for this research came from European Research Council (ERC). The news correspondents obtained a quote from the research from Sorbonne University, "The force field is capable of reproducing the structure of ZIF-4, ZIF-zni and the amorphous (ZIF_a) and liquid (ZIF_liq) phases that respectively result when these crystalline materials are heated. Symmetry functions computed over a database of structures of the four phases are used as inputs to train a neural network that predicts the probabilities of belonging to each of the four phases at the local Zn2+ level with 90% accuracy. We apply this methodology to follow the time-evolution of the amorphization of ZIF-4 and the melting of ZIF-zni along a series of molecular dynamics trajectories. We first computed the transition temperature and determined the associated thermodynamic state functions. Subsequently, we studied the mechanisms. Both processes consist of two steps: (ⅰ) for ZIF-4, a low-density amorphous phase is first formed, followed by the final ZIF_a phase while (ⅱ) for ZIF-zni, a ZIF_a-like phase precedes the formation of the liquid phase. These processes involve connectivity changes in the first neighbour ligands around the central Zn2_+ cations."
NETL
NSTL
Royal Soc Chemistry