Structure prediction of boron-doped graphene by machine learning.

Heteroatom doping has endowed graphene with manifold aspects of material properties and boosted its applications. The atomic structure determination of doped graphene is vital to understand its material properties. Motivated by the recently synthesized boron-doped graphene with relatively high concentration, here we employ machine learning methods to search the most stable structures of doped boron atoms in graphene, in conjunction with the atomistic simulations. From the determined stable structures, we find that in the free-standing pristine graphene, the doped boron atoms energetically prefer to substitute for the carbon atoms at different sublattice sites and that the para configuration of boron-boron pair is dominant in the cases of high boron concentrations. The boron doping can increase the work function of graphene by 0.7 eV for a boron content higher than 3.1%.

MDTS: automatic complex materials design using Monte Carlo tree search.

Complex materials design is often represented as a black-box combinatorial optimization problem. In this paper, we present a novel python library called MDTS (Materials Design using Tree Search). Our algorithm employs a Monte Carlo tree search approach, which has shown exceptional performance in computer Go game. Unlike evolutionary algorithms that require user intervention to set parameters appropriately, MDTS has no tuning parameters and works autonomously in various problems. In comparison to a Bayesian optimization package, our algorithm showed competitive search efficiency and superior scalability. We succeeded in designing large Silicon-Germanium (Si-Ge) alloy structures that Bayesian optimization could not deal with due to excessive computational cost. MDTS is available at https://github.com/tsudalab/MDTS.