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CO 2 Reduction to Methane and Ethylene on a Single-Atom Catalyst: A Grand Canonical Quantum Mechanics Study
Not Available
Grand canonical inverse design of multicomponent colloidal crystals
Inverse design methods are powerful computational approaches for creating colloidal systems which self-assemble into a target morphology by reverse engineering the Hamiltonian of the system. Despite this, these optimization procedures tend to yield Hamiltonians which are too complex to be experimentally realized. An alternative route to complex structures involves the use of several different components, however, conventional inverse design methods do not explicitly account for the possibility of phase separation into compositionally distinct structures. We present an inverse design scheme for multicomponent colloidal systems by combining active learning with a method to directly compute their ground state phase diagrams. This explicitly accounts for phase separation and can locate stable regions of Hamiltonian parameter space which grid-based surveys are prone to miss. Using this we design low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting. This reinforces the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity, enabling the assembly of non-trivial structures with only few simple components instead of a single complex one.
Understanding the fast kinetics and mechanism of sodium storage in antimony using ab initio grand canonical Monte Carlo simulation and operando X-ray scattering
Using ai-GCMC simulations,operandoWAXS, and kinetics analysis, we found that the high-rate performance of Sb as an alloy anode in Na-ion batteries is due to the presence of an amorphous intermediate phase formed during sodiation and desodiation.
Finite-temperature many-body perturbation theory in the grand canonical ensemble
A finite-temperature many-body perturbation theory is presented, which expands in power series the electronic grand potential, chemical potential, internal energy, and entropy on an equal footing. Sum-over-states and sum-over-orbitals analytical formulas for the second-order perturbation corrections to these thermodynamic properties are obtained in a time-independent, nondiagrammatic, algebraic derivation, relying on the sum rules of the Hirschfelder–Certain degenerate perturbation energies in a degenerate subspace as well as nine algebraic identities for the zeroth-order thermal averages of one- through four-indexed quantities and products thereof. Lastly, they reproduce numerically exactly the benchmark data obtained as the numerical derivatives of the thermal-full-configuration-interaction results for a wide range of temperatures.
Self-consistent calculations of the electric charge, ion drag force, and the drift velocity of spherical grains using Langevin dynamics and comparisons against canonical experiments
We present trajectory simulation-based modeling to capture the interactions between ions and charged grains in dusty or complex plasmas. Our study is motivated by the need for a self-consistent and experimentally validated approach for accurately calculating the ion drag force and grain charge that determine grain collective behavior in plasmas. We implement Langevin dynamics in a computationally efficient predictor–corrector approach to capture multiscale ion and grain dynamics. Predictions of grain velocity, grain charge, and ion drag force are compared with prior measurements to assess our approach. The comparisons reveal excellent agreement to within ±20% between predicted and measured grain velocities [Yaroshenko et al., Phys. Plasmas 12, 093503 (2005) and Khrapak et al., Europhys. Lett. 97, 35001 (2012)] for 0.64, 1.25 μm grains at ∼20−500 Pa. Comparisons with the measured grain charge [Khrapak et al., Phys. Rev. E 72, 016406 (2005)] under similar conditions reveal agreement to within ∼20% as well. Measurements of the ion drag force [Hirt et al., Phys. Plasmas 11, 5690 (2004); IEEE Trans. Plasma Sci. 32, 582 (2004)] are used to assess the viability of the presented approach to calculate the ion drag force experienced by grains exposed to ion beams of well-defined energy. Excellent agreement between calculations and measurements is obtained for beam energies >10 eV, and the overprediction below 10 eV is attributed to the neglect of charge exchange collisions in our modeling. Along with critical assessments of our approach, suggestions for future experimental design to probe charging of and momentum transfer onto grains that capture the effect of space charge concentration and external fields are outlined.
Highly efficient parallel grand canonical simulations of interstitial-driven diffusion-deformation processes
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Bayesian inference of the dense-matter equation of state encapsulating a first-order hadron-quark phase transition from observables of canonical neutron stars
The remarkable progress in recent multimessenger observations of both isolated neutron stars (NSs) and their mergers has provided some of the much needed data to improve our understanding about the equation of state (EOS) of dense neutron-rich matter. Various EOSs with or without some kinds of phase transitions from hadronic to quark matter (QM) have been widely used in many forward modelings of NS properties. Direct comparisons of these predictions with observational data sometimes also using χ 2 minimizations have provided very useful constraints on the model EOSs. Furthermore, it is normally difficult to perform uncertain quantifications and analyze correlations of the EOS model parameters involved in forward modelings especially when the available data are still very limited.
Neural Canonical Transformation with Symplectic Flows
Not Available
Low-Communication Asynchronous Distributed Generalized Canonical Polyadic Tensor Decomposition.
Abstract not provided.
Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations
We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE)-originally designed for nonlinear dimensionality reduction-for shape correspondence. The key idea is to find dense correspondences between shapes by first obtaining high-dimensional neighborhood-preserving embeddings of low-dimensional point clouds and subsequently aligning the source and target embeddings using locally linear transformations. We demonstrate that learning the embedding using a new LLE-inspired point cloud reconstruction objective results in accurate shape correspondences. More specifically, the approach comprises an end-to-end learnable framework of extracting high-dimensional neighborhood-preserving embeddings, estimating locally linear transformations in the embedding space, and reconstructing shapes via divergence measure-based alignment of probability density functions built over reconstructed and target shapes. Our approach enforces embeddings of shapes in correspondence to lie in the same universal/canonical embedding space, which eventually helps regularize the learning process and leads to a simple nearest neighbors approach between shape embeddings for finding reliable correspondences. Comprehensive experiments show that the new method makes noticeable improvements over state-of-the-art approaches on standard shape correspondence benchmark datasets covering both human and nonhuman shapes.
High-Speed Flow Visualization of a Canonical Airblast Atomizer Using Synchrotron X-Rays
Abstract not provided
Efficient Bounds and Estimates for Canonical Angles in Randomized Subspace Approximations
Not provided.
NCAP - Non-Canonical Amino Acid Parameterization
Our software is designed to only work on novel NCAA side chains and parameterize for the CHARMM potential. This works around the detailed analysis and manual parameter fitting of many existing software programs. Our approach is also more specific than those used in Rosetta where we calculate parameters specific for each NCAA rather than assuming transferability.