Computing Sciences

The discovery of ~80 K superconductivity in nickelate La3Ni2O7 under pressure has ignited intense interest. Here, we present a comprehensive first-principles study of the electron-phonon (e-ph) coupling in La3Ni2O7 and its implications on the observed superconductivity. Our results conclude that the e-ph coupling is too weak (with a coupling constant λ ≲ 0.5) to account for the high Tc, albeit interesting many-electron correlation effects exist. While Coulomb interactions (via GW self-energy and Hubbard U) enhance the e-ph coupling strength, electron doping (oxygen vacancies) introduces no major changes. Additionally, different structural phases display varying characteristics near the Fermi level, but do not alter the conclusion. The e-ph coupling landscape of La3Ni2O7 is intrinsically different from that of infinite-layer nickelates. These findings suggest that a phonon-mediated mechanism is unlikely to be responsible for the observed superconductivity in La3Ni2O7, pointing instead to an unconventional nature.

Subspace projection methods utilizing perturbative corrections have been proposed for computing the lowest few eigenvalues and corresponding eigenvectors of large Hamiltonian matrices. In this paper, we build upon these methods and introduce the term Subspace Projection with Perturbative Corrections (SPPC) method to refer to this approach. We tailor the SPPC for nuclear many-body Hamiltonians represented in a truncated configuration interaction subspace, i.e., the no-core shell model (NCSM). We use the hierarchical structure of the NCSM Hamiltonian to partition the Hamiltonian as the sum of two matrices. The first matrix corresponds to the Hamiltonian represented in a small configuration space, whereas the second is viewed as the perturbation to the first matrix. Eigenvalues and eigenvectors of the first matrix can be computed efficiently. Because of the split, perturbative corrections to the eigenvectors of the first matrix can be obtained efficiently from the solutions of a sequence of linear systems of equations defined in the small configuration space. These correction vectors can be combined with the approximate eigenvectors of the first matrix to construct a subspace from which more accurate approximations of the desired eigenpairs can be obtained. We show by numerical examples that the SPPC method can be more efficient than conventional iterative methods for solving large-scale eigenvalue problems such as the Lanczos, block Lanczos and the locally optimal block preconditioned conjugate gradient (LOBPCG) method. The method can also be combined with other methods to avoid convergence stagnation.

ABSTRACT: We present AMR‐Wind, a verified and validated high‐fidelity computational‐fluid‐dynamics code for wind farm flows. AMR‐Wind is a block‐structured, adaptive‐mesh, incompressible‐flow solver that enables predictive simulations of the atmospheric boundary layer and wind plants. It is a highly scalable code designed for parallel high‐performance computing with a specific focus on performance portability for current and future computing architectures, including graphical processing units (GPUs). In this paper, we detail the governing equations, the numerical methods, and the turbine models. Establishing a foundation for the correctness of the code, we present the results of formal verification and validation. The verification studies, which include a novel actuator line test case, indicate that AMR‐Wind is spatially and temporally second‐order accurate. The validation studies demonstrate that the key physics capabilities implemented in the code, including actuator disk models, actuator line models, turbulence models, and large eddy simulation (LES) models for atmospheric boundary layers, perform well in comparison to reference data from established computational tools and theory. We conclude with a demonstration simulation of a 12‐turbine wind farm operating in a turbulent atmospheric boundary layer, detailing computational performance and realistic wake interactions.

Dexterous manipulation remains an aspirational goal for autonomous robotic systems, particularly when learning to lift and rotate objects against gravity with intermittent finger contacts. We use model-free reinforcement learning to compare the effect of curriculum (i.e., combinations of lift and rotation tasks) and haptic information (i.e., no-tactile versus 3D-force) on learning with a simulated three-finger robotic hand. In addition, a novel curriculum-based learning rate scheduler accelerates convergence. We demonstrate that the choice of curriculum biases the progression of learning for dexterous manipulation across objects with different weights, sizes, and shapes-underscoring the robustness of our learning approach. Unexpectedly, learning is achieved even in the absence of haptic information. This challenges conventional thinking about task complexity and the necessity of haptic information for dexterous manipulation for this task. This work invites the analogy of curriculum learning as a malleable developmental process from a pluripotent state driven by the nature of the learning experience.

We present a fully-integrated lattice Boltzmann (LB) method for fluid–structure interaction (FSI) simulations that efficiently models deformable solids in complex suspensions and active systems. Our Eulerian method (LBRMT) couples finite-strain solids to the LB fluid on the same fixed computational grid with the reference map technique (RMT). An integral part of the LBRMT is a new LB boundary condition for moving deformable interfaces across different densities. With this fully Eulerian solid–fluid coupling, the LBRMT is well-suited for parallelization and simulating multi-body contact without remeshing or extra meshes. We validate its accuracy via a benchmark of a deformable solid in a lid-driven cavity, then showcase its versatility through examples of soft solids rotating and settling. The LBRMT achieves a spatial convergence rate between first-order and second-order for FSI simulations and is designed for low to intermediate Reynolds number flows with finite inertia at small Mach numbers. With simulations of complex suspensions mixing, we highlight the potential of the LBRMT for studying collective behavior in soft matter and biofluid dynamics.

The efficient removal of CO₂ from exhaust streams and even directly from air is necessary to forestall climate change, lending urgency to the search for new materials that can rapidly capture CO₂ at high capacity. The recent discovery that diamine-appended metal-organic frameworks can exhibit cooperative CO₂ uptake via the formation of ammonium carbamate chains begs the question of whether simple organic polyamine molecules could be designed to achieve a similar switch-like behavior with even higher separation capacities. Here, we present a solid molecular triamine, 1,3,5-tris(aminomethyl)benzene (TriH), that rapidly captures large quantities of CO₂ upon exposure to humid air to form the porous, crystalline, ammonium carbamate network solid TriH(CO₂)_1.5·xH₂O (TriHCO₂). The phase transition behavior of TriH converting to TriHCO₂ was studied through powder and single-crystal X-ray diffraction analysis, and additional spectroscopic techniques further verified the formation of ammonium carbamate species upon exposing TriH to humid air. Detailed breakthrough analyses conducted under varying temperatures, relative humidities, and flow rates reveal record CO₂ absorption capacities as high as 8.9 mmol/g. Computational analyses reveal an activation barrier associated with TriH absorbing CO₂ under dry conditions that is lowered under humid conditions through hydrogen bonding with a water molecule in the transition state associated with N-C bond formation. These results highlight the prospect of tunable molecular polyamines as a new class of candidate absorbents for high-capacity CO₂ capture.

The use of machine learning (ML) to refine low-level theoretical calculations to achieve higher accuracy is a promising and actively evolving approach known as Δ-ML. The density matrix renormalization group (DMRG) is a powerful variational approach widely used for studying strongly correlated quantum systems. High computational efficiency can be achieved without compromising accuracy. Here, we demonstrate the potential of a simple ML model to significantly enhance the performance of the quantum chemical DMRG method.