Reinforcement Learning-Guided Long-Timescale Simulation of Hydrogen Transport in Metals.

Diffusion in alloys is an important class of atomic processes. However, atomistic simulations of diffusion in chemically complex solids are confronted with the timescale problem: the accessible simulation time is usually far shorter than that of experimental interest. In this work, long-timescale simulation methods are developed using reinforcement learning (RL) that extends simulation capability to match the duration of experimental interest. Two special limits, RL transition kinetics simulator (TKS) and RL low-energy states sampler (LSS), are implemented and explained in detail, while the meaning of general RL are also discussed. As a testbed, hydrogen diffusivity is computed using RL TKS in pure metals and a medium entropy alloy, CrCoNi, and compared with experiments. The algorithm can produce counter-intuitive hydrogen-vacancy cooperative motion. We also demonstrate that RL LSS can accelerate the sampling of low-energy configurations compared to the Metropolis-Hastings algorithm, using hydrogen migration to copper (111) surface as an example.

Experimental Studies on the Dynamic Memcapacitance Modulation of the ReO3@ReS2 Composite Material-Based Diode.

In this study, both memcapacitive and memristive characteristics in the composite material based on the rhenium disulfide (ReS2) rich in rhenium (VI) oxide (ReO3) surface overlayer (ReO3@ReS2) and in the indium tin oxide (ITO)/ReO3@ReS2/aluminum (Al) device configuration is presented. Comprehensive experimental analysis of the ReO3@ReS2 material properties' dependence on the memcapacitor electrical characteristics was carried out by standard as well as frequency-dependent current-voltage, capacitance-voltage, and conductance-voltage studies. Furthermore, determination of the charge carrier conduction model, charge carrier mobility, density of the trap states, density of the available charge carrier, free-carrier concentration, effective density of states in the conduction band, activation energy of the carrier transport, as well as ion hopping was successfully conducted for the ReO3@ReS2 based on the experimental data. The ITO/ReO3@ReS2/Al charge carrier conduction was found to rely on the mixed electronic-ionic processes, involving electrochemical metallization and lattice oxygen atoms migration in response to the externally modulated electric field strength. The chemical potential generated by the electronic-ionic ITO/ReO3@ReS2/Al resistive memory cell non-equlibrium processes leads to the occurrence of the nanobattery effect. This finding supports the possibility of a nonvolatile memory cell with a new operation principle based on the potential read function.