In this communication, we present a new approach towards RT-TDDFT through time-dependent KS equations based on an \emph{adiabatic eigenstate subspace} (AES) procedure. It introduces a second-order split operator technique in energy representation to implement the approximate TD propagator in AES. Most of the elements in TDKS matrix are directly computed in Cartesian coordinate grid (CCG). To demonstrate the internal consistency of our proposed scheme, we computed the TD dipole moment and high harmonic generation spectra using an adiabatic local density approximation. The comparison with available theoretical results ensures the feasibility of this proposed route.
We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries. This method is applied to Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays. In both cases the SNN reveals phase boundaries consistent with prior research. The combination of leveraging the power of feed-forward neural networks, unsupervised learning and the ability to learn about multiple phases without knowing about their existence provides a powerful method to explore new and unknown phases of matter.
The Hartree-Fock problem provides the conceptual and mathematical underpinning of a large portion of quantum chemistry. As efforts in quantum technology aim to enhance computational chemistry algorithms, the fundamental Hartree-Fock problem is a natural target. While quantum computers and quantum simulation offer many prospects for the future of modern chemistry, the Hartree-Fock problem is not a likely candidate. We highlight this fact from a number of perspectives including computational complexity, practical examples, and the full characterization of the energy landscapes for simple systems.
We demonstrate that the effective Hamiltonians obtained with the downfolding procedure based on double unitary coupled cluster (DUCC) ansatz can be used in the context of Greens function coupled cluster (GFCC) formalism to calculate spectral functions of molecular systems. This combined approach (DUCC-GFCC) provides a significant reduction of numerical effort and good agreement with the corresponding all-orbital GFCC methods in energy windows that are consistent with the choice of active space. These features are demonstrated on the example of two benchmark systems: H2O and N2, where DUCC-GFCC calculations were performed for active spaces of various sizes.
In quantum many-body problems, one of the main difficulties comes from the description of non-negligible interactions which require, at least in principle, an exponential amount of information. Recently, in the context of spin glasses and Boltzmann machines, it has been demonstrated that systematic machine learning of the wave function can reduce these issues to a tractable computational problem. In this work, we apply this approach to a different situation, i.e. the problem of finding the ground state of a given quantum system made of electrons, entirely described by its Hamiltonian operator, and by utilizing feedforward neural networks. Although still in the shape of a proof of concept, one can already observe that this seminal idea is able to substantially simplify the complexity of this peculiar, and important, problem.
McNeece Colin, Raynaud Xavier, Nilsen Halvor
et al.
The study of geological systems requires the solution of complex geochemical relations. We present an implementation of a chemical solver which can handle various types of models, including surface chemistry. The implementation is done in view of easy coupling with flow simulations to obtain a fully-coupled, fully-implicit solver for chemical reaction transport equations applicable to realistic reservoir models.
The reliability is of the most importance when employing a numerical method to solve the eigenvalue integral equations. In this paper, we present one type of particular singularities (pseudosingularities) existing in eigenvalue integral equations which will impair even destroy the reliability of the numerical solutions in an implicit way. Two odd phenomena emerging in the numerical eigenvalues and the corresponding eigenfunctions are reviewed. And the relations between the pseudosingularities, the odd phenomena and the reliability of the obtained numerical results are discussed.
We report the effect of lipid head-group dipole orientation on phase behaviour of phospholipid assembly. The work explains molecular-scale mechanism of ion-lipid, anesthetic-lipid interactions where reorientation of dipoles play important role in membrane potential modification. Molecular Dynamics simulations are performed to analyse structure-property relationship and dynamical behaviour of lipid biomembranes considering coarse-grained model interactions.
We demonstrate that extending the Shadow Wave Function to fermionic systems facilitates to accurately calculate strongly-correlated multi-reference systems such as the stretched H2 molecule. This development considerably extends the scope of electronic structure calculations and enables to efficiently recover the static correlation energy using just a single Slater determinant.
In the construction of simulations of rear-end vehicle impacts, the Articulated Total Body (ATB) software package can be a useful tool. In this article we discuss the effect of using artificially inflated values for seat-backrest stiffness in ATB simulations. We will also present methods for quickly assessing the quality of simulation results. In this connection, we will discuss the perils of using the default contact-force models that are included in ATB package releases.
This study develops a ray technique for determining the resonance frequencies of triangular membranes. The technique is demonstrated for homogenous rectangular and triangular membranes with fixed boundaries. Where possible, the results are compared with exact calculations. The membrane resonances are calculated using an equivalent string whose length is proportional to the reciprocal of the length of closed paths starting from an arbitrary point within the membrane. Closed paths are ray paths which arrive back at the starting point going in the same direction.
First principles calculations were performed to study strain effects on band gap of armchair graphene nanoribbons (AGNRs)with different edge passivation, including H, O, and OH group. The band gap of the H-passivated AGNRs shows a nearly periodic zigzag variation under strain. For O and OH passivation, the zigzag patterns are significantly shifted by a modified quantum confinement due to the edges. In addition, the band gap of the O-passivated AGNRs experiences a direct-to-indirect transition with sufficient tensile strain (~5%). The indirect gap reduces to zero with further increased strain.
An extension to computational mechanics complexity measure is proposed in order to tackle quantum states complexity quantification. The method is applicable to any $n-$partite state of qudits through some simple modifications. A Werner state was considered to test this approach. The results show that it undergoes a phase transition between entangled and separable versions of itself. Also, results suggest interplay between quantum state complexity robustness rise and entanglement. Finally, only via symbolic dynamics statistical analysis, the proposed method was able to distinguish separable and entangled dynamical structural differences.