The fluid-filled semicircular canals (SCCs) of the vestibular system are used by all vertebrates to sense angular rotation. Despite masses spanning seven decades, all mammalian SCCs are nearly the same size. We propos...
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The fluid-filled semicircular canals (SCCs) of the vestibular system are used by all vertebrates to sense angular rotation. Despite masses spanning seven decades, all mammalian SCCs are nearly the same size. We propose that the SCC represents a sensory organ that evolution has “optimally designed.” Four geometric parameters characterize the SCC, and “building materials” of given physical properties are assumed. Identifying physical and physiological constraints on SCC operation, we find the most sensitive SCC has dimensions consistent with available data. Since natural selection involves optimization, this approach may find broader use in understanding biological structures.
Electron capture between solar wind ions and neutral species has contributed to the understanding of X-ray production from solar system *** charge transfer and excitation processes in solar wind ions of He^(+)(1 s) co...
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Electron capture between solar wind ions and neutral species has contributed to the understanding of X-ray production from solar system *** charge transfer and excitation processes in solar wind ions of He^(+)(1 s) colliding with Li(1 s^(2)2 s) atoms are studied by utilizing the full quantum-mechanical molecular-orbital close-coupling(QMOCC) method with impact energies of 0.003-2 keV *** of cross sections from single-and multi-configurational calculations for a selfconsistent field(SCF and MCSCF) process are carried *** show that the dominant reaction channels are He(1 s2 l ^(1,3) L)+Li^(+)(1 s^(2) ^(1) S).Good consistency is found among present total and state-selective charge transfer and excitation cross sections with other theoretical and experimental data in the same energy *** to the differences between coupling matrix elements in high-energy states,the charge transfer cross sections calculated from SCF and MCSCF split slightly as E> 0.4 keV *** Stueckelberg oscillations for charge transfer appear in the present *** addition,the differences of cross sections for electron excitation to Li(ls^(2)2 p) in the singlet/triplet molecular states with He+(1 s) are much smaller than those of charge transfer processes because of the similar energy gaps from Li(ls^(2)2 p) to the ground state in singlet/triplet states in the large R region.
Galaxies form and evolve via a multitude of complex physics. In this work, we investigate the role of cosmic ray (CR) feedback in galaxy evolution and reionisation, by examining its impact on the escape of ionising ra...
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Single particle cryo-electron microscopy (EM) is an increasingly popular method for determining the 3-D structure of macromolecules from noisy 2-D images of single macromolecules whose orientations and positions are r...
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Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential ene...
Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.
We propose the coarse-grained spectral projection method (CGSP), a deep learning-assisted approach for tackling quantum unitary dynamic problems with an emphasis on quench dynamics. We show CGSP can extract spectral c...
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We establish a scale separation of Kolmogorov width type between subspaces of a given Banach space under the condition that a sequence of linear maps converges much faster on one of the subspaces. The general techniqu...
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We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-source machine learning library, and ...
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We introduce the Deep Post–Hartree–Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predict...
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We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we develop a...
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