Abstract
An atomistic structural model for melt-quenched BO glass has eluded the simulation community so far. The difficulty lies in the abundance of the six-membered boroxol rings - an intermediate-range order motif suggested through Raman and NMR spectroscopy - which is challenging to obtain in atomistic molecular dynamics simulations. Here, we report the development of a DFT-accurate machine-learned potential for BO and employ quench rates as low as 10 K/s to obtain BO glasses with more than 30% of boron atoms in boroxol rings. Also, we show that the pressure, and consequently the boroxol fraction, in the deep potential molecular dynamics (DPMD) simulations critically depends on the range of the geometry descriptor used in the embedding neural network, and at least a 9 range is required. The boroxol ring fraction increases with decreasing quench rate. Finally, amorphous BO configurations display a minimum in energy at a boroxol fraction of 75%, intriguingly close to the experimental estimate in BO glass.