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Published: May 2019
Published: May 2019
Authors: Alexey V. Onufriev, David A. Case
Published: May 2019
Authors: Alexey V. Onufriev, David A. Case
Published: May 2019
It would often be useful in computer simulations to use an implicit description of solvation effects, instead of explicitly representing the individual solvent molecules. Continuum dielectric models often work well in describing the thermodynamic aspects of aqueous solvation and can be very efficient compared to the explicit treatment of the solvent. Here, we review a particular class of so-called fast implicit solvent models, generalized Born (GB) models, which are widely used for molecular dynamics (MD) simulations of proteins and nucleic acids. These approaches model hydration effects and provide solvent-dependent forces with efficiencies comparable to molecular-mechanics calculations on the solute alone; as such, they can be incorporated into MD or other conformational searching strategies in a straightforward manner. The foundations of the GB model are reviewed, followed by examples of newer, emerging models and examples of important applications. We discuss their strengths and weaknesses, both for fidelity to the underlying continuum model and for the ability to replace explicit consideration of solvent molecules in macromolecular simulations.
Authors: Hennigh, Oliver, et al
Published: Dec 2020
Authors: Hennigh, Oliver, et al
Published: Dec 2020
We present SimNet, an AI-driven multi-physics simulation framework, to
accelerate simulations across a wide range of disciplines in science and
engineering. Compared to traditional numerical solvers, SimNet addresses a wide
range of use cases - coupled forward simulations without any training data,
inverse and data assimilation problems. SimNet offers fast turnaround time by
enabling parameterized system representation that solves for multiple
configurations simultaneously, as opposed to the traditional solvers that solve
for one configuration at a time. SimNet is integrated with parameterized
constructive solid geometry as well as STL modules to generate point clouds.
Furthermore, it is customizable with APIs that enable user extensions to
geometry, physics and network architecture. It has advanced network
architectures that are optimized for high-performance GPU computing, and offers
scalable performance for multi-GPU and multi-Node implementation with
accelerated linear algebra as well as FP32, FP64 and TF32 computations. In this
paper we review the neural network solver methodology, the SimNet architecture,
and the various features that are needed for effective solution of the PDEs. We
present real-world use cases that range from challenging forward multi-physics
simulations with turbulence and complex 3D geometries, to industrial design
optimization and inverse problems that are not addressed efficiently by the
traditional solvers. Extensive comparisons of SimNet results with open source
and commercial solvers show good correlation.
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Authors: Hu, Xiao-Min, et al
Published: Dec 2020
Authors: Hu, Xiao-Min, et al
Published: Dec 2020
Authors: Philip Ball
Published: Dec 2020
Authors: Philip Ball
Published: Dec 2020
- A team in China claims to have made the first definitive demonstration of ‘quantum advantage’ — exploiting the counter-intuitive workings of quantum mechanics to perform computations that would be prohibitively slow on classical computers.
- Last year, researchers at Google’s quantum-computing laboratory in Santa Barbara, California, announced the first-ever demonstration of quantum advantage.
Published: Feb 2021
Published: Feb 2021
A quantitative phase-field model is developed for prediction of solute trapping for solidification velocities relevant to the additive manufacturing. An anti-trapping flux is proposed to generate a chemical potential jump independent of the interface width and consistent with the sharp interface continuous growth (CG) model. The thin-interface analysis up to the second order is implemented to quantitatively parametrize the phase-field model based on the material properties for both full and zero solute drag limits of the CG model. As a benchmark example, the experimental data on Si-9at.%As (Kittl et al., Acta Materialia, 2000) is used to compare the partition coefficient and kinetic undercooling predicted by this phase-field model with those of the CG model. Our results, especially with the full-drag limit, present a very good agreement with the experimental data and theoretical models for solidification velocities up to the diffusive velocity. Unlike other phase-field models, this proposed model predicts accurate partition coefficient and kinetic undercooling for a wide range of solidification velocities, and the results are less sensitive to the diffusive interface width, enabling quantitative simulations in larger length scales. The model performance in prediction of the cellular growth is highlighted by showing that the primary dendritic arm spacing is also weakly dependent on the diffusive interface width.
Published: Sep 2020
Published: Sep 2020
The electronic Schrödinger equation can only be solved analytically for the hydrogen atom, and the numerically exact full configuration-interaction method is exponentially expensive in the number of electrons. Quantum Monte Carlo methods are a possible way out: they scale well for large molecules, they can be parallelized and their accuracy has, as yet, been only limited by the flexibility of the wavefunction ansatz used. Here we propose PauliNet, a deep-learning wavefunction ansatz that achieves nearly exact solutions of the electronic Schrödinger equation for molecules with up to 30 electrons. PauliNet has a multireference Hartree–Fock solution built in as a baseline, incorporates the physics of valid wavefunctions and is trained using variational quantum Monte Carlo. PauliNet outperforms previous state-of-the-art variational ansatzes for atoms, diatomic molecules and a strongly correlated linear H10, and matches the accuracy of highly specialized quantum chemistry methods on the transition-state energy of cyclobutadiene, while being computationally efficient.
Authors: Francesco Caravelli, Bin Yan, Luis Pedro Garcia-Pintos, Alioscia Hamma
Published: Dec 2020
Authors: Francesco Caravelli, Bin Yan, Luis Pedro Garcia-Pintos, Alioscia Hamma
Published: Dec 2020
We study the role of coherence in closed and open quantum batteries. We
obtain upper bounds to the work performed or energy exchanged by both closed
and open quantum batteries in terms of coherence. Specifically, we show that
the energy storage can be bounded by the Hilbert-Schmidt coherence of the
density matrix in the spectral basis of the unitary operator that encodes the
evolution of the battery. We also show that an analogous bound can be obtained
in terms of the battery's Hamiltonian coherence in the basis of the unitary
operator by evaluating their commutator. We apply these bounds to a 4-state
quantum system and the anisotropic XY Ising model in the closed system case,
and the Spin-Boson model in the open case.
Authors: Alan C. Santos
Published: Dec 2020
Authors: Alan C. Santos
Published: Dec 2020
Devices that use quantum advantages for storing energy in the degree of
freedom of quantum systems have drawn attention due to their properties of
working as quantum batteries. However, one can identify a number of problems
that need to be adequately solved before a real manufacturing process of these
devices. In particular, it is important paying attention to the ability of
quantum batteries in storing energy when no consumption center is connected to
them. In this paper, by considering quantum batteries disconnected from
external charging fields and consumption center, we study the decoherence
effects that lead to charge leakage to the surrounding environment. We identify
this phenomena as a self-discharging of QBs, in analogy to the inherent decay
of the stored charge of conventional classical batteries in a open-circuit
configuration. The quantum advantage concerning the classical counterpart is
highlighted for single- and multi-cell quantum batteries.
From Paper: Four‐Wave Mixing Response via Hybrid Coulomb‐Coupled Cavity Optomechanics
Authors: Muhib Ullah, Farhan Saif, Li‐Gang Wang
Published: Jun 2020
From Paper: Four‐Wave Mixing Response via Hybrid Coulomb‐Coupled Cavity Optomechanics
Authors: Muhib Ullah, Farhan Saif, Li‐Gang Wang
Published: Jun 2020
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