Phase diagram of doped BaFe2_2As2_2 superconductor under broken C4C_4 symmetry

We develop a minimal multiorbital tight-binding model with realistic hopping parameters. The model breaks the symmetry of the tetragonal point group by lowering it from $C_4$ to $D_{2d}$, which accurately describes the Fermi surface evolution of the electron-doped BaFe$_{2-x}$Co$_x$As$_2$ and hole-doped Ba$_{1-y}$K$_y$Fe$_2$As$_2$ compounds. An investigation of the phase diagram with a mean-field $t$-$U$-$V$ Bogoliubov-de Gennes Hamiltonian results in agreement with the experimentally observed electron- and hole-doped phase diagram with only one set of $t$, $U$ and $V$ parameters. Additionally, the self-consistently calculated superconducting order parameter exhibits $s^\pm$-wave pairing symmetry with a small d-wave pairing admixture in the entire doping range, % The superconducting $s^\pm + d$-wave order parameter which is the subtle result of the weakly broken symmetry and competing interactions in the multiorbital mean-field Hamiltonian

DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization

Recent algorithms designed for reinforcement learning tasks focus on finding a single optimal solution. However, in many practical applications, it is important to develop reasonable agents with diverse strategies. In this paper, we propose Diversity-Guided Policy Optimization (DGPO), an on-policy framework for discovering multiple strategies for the same task. Our algorithm uses diversity objectives to guide a latent code conditioned policy to learn a set of diverse strategies in a single training procedure. Specifically, we formalize our algorithm as the combination of a diversity-constrained optimization problem and an extrinsic-reward constrained optimization problem. And we solve the constrained optimization as a probabilistic inference task and use policy iteration to maximize the derived lower bound. Experimental results show that our method efficiently finds diverse strategies in a wide variety of reinforcement learning tasks. We further show that DGPO achieves a higher diversity score and has similar sample complexity and performance compared to other baselines

Strain rate dependent mechanical properties in single crystal nickel nanowires

We measure the strain rate dependence of 0.2% offset yield stress in single-crystal nickel nanowires with diameters ranging from 80 to 300 nm. In situ tensile experiments with strain rates from 10 4 s 1 to 10 2 s 1 were conducted, and the small activation volume ( 10b3, where b is the Burgers vector length) and high strain-rate sensitivity ( 0.1) were obtained. These results agreed with atomistic simulations. Our work provides insights into the strength-limiting and rate-controlling mechanism of plasticity at the nanoscale

Atomistic characterization of pseudoelasticity and shape memory in NiTi nanopillars

Abstract Molecular dynamics simulations are performed to study the atomistic mechanisms governing the pseudoelasticity and shape memory in nickel-titanium (NiTi) nanostructures. For a h1 1 0i -oriented nanopillar subjected to compressive loading-unloading, we observe either a pseudoelastic or shape memory response, depending on the applied strain and temperature that control the reversibility of phase transformation and deformation twinning. We show that irreversible twinning arises owing to the dislocation pinning of twin boundaries, while hierarchically twinned microstructures facilitate the reversible twinning. The nanoscale size effects are manifested as the load serration, stress plateau and large hysteresis loop in stress-strain curves that result from the high stresses required to drive the nucleationcontrolled phase transformation and deformation twinning in nanosized volumes. Our results underscore the importance of atomistically resolved modeling for understanding the phase and deformation reversibilities that dictate the pseudoelasticity and shape memory behavior in nanostructured shape memory alloys