No abstract

Two-dimensional (2D) materials with nanometer-size holes are promising systems for DNA sequencing, water purification, and molecule selection/separation. However, controllable creation of holes with uniform sizes and shapes is still a challenge, especially when the 2D material consists of several atomic layers as, e.g., MoS 2 , the archetypical transition metal dichalcogenide. We use analytical potential molecular dynamics simulations to study the response of 2D MoS 2 to cluster irradiation. We model both freestanding and supported sheets and assess the amount of damage created in MoS 2 by the impacts of noble gas clusters in a wide range of cluster energies and incident angles. We show that cluster irradiation can be used to produce uniform holes in 2D MoS 2 with the diameter being dependent on cluster size and energy. Energetic clusters can also be used to displace sulfur atoms preferentially from either top or bottom layers of S atoms in MoS 2 and also clean the surface of MoS 2 sheets from adsorbents. Our results for MoS 2 , which should be relevant to other 2D transition metal dichalcogenides, suggest new routes toward cluster beam engineering of devices based on 2D inorganic materials.

Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free-energy functions using data of various origins. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on a Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with the existing studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.