The Quality of the Embedding Potential Is Decisive for Minimal Quantum Region Size in Embedding Calculations: The Case of the Green Fluorescent Protein
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Deep learning has surged in popularity and proven to be effective for various artificial intelligence applications including information extraction from cancer pathology reports. Since word representation is a core unit that enables deep learning algorithms to understand words and be able to perform NLP, this representation must include as much information as possible to help these algorithms achieve high classification performance. Therefore, in this work in addition to the distributional information of words in large sized corpora, we use UMLS vocabulary resources to enrich the vector space representation of words with the semantic relations between words. These resources provide many terminologies pertaining to cancer. The refined word embeddings are used with a convolutional neural (CNN) model to extract four data elements from cancer pathology reports; ICD-O-3 tumor topography codes, tumor laterality, behavior, and histological grade. We observed that using UMLS vocabulary resources to enrich word embeddings of CNN models consistently outperformed CNN models without pre-training word embeddings and even with pre-trained word embeddings on a domain specific corpus across all four tasks. The results show marginal improvement on the laterality task, but a significant improvement on the other tasks, especially for the macro-f score. Specifically, the improvements are 3%, 13%, and 15% for tumor site, histological grade, and behavior tasks, respectively. This approach is encouraging to enrich word embeddings with more clinical data resources to be used for information abstraction tasks from clinical pathology reports.
Frozen-Density-Embedding Theory (FDET) is a formalism to obtain the upper bound of the ground-state energy of the total system and the corresponding embedded wavefunction by means of Euler-Lagrange equations [T. A. Wesolowski, Phys. Rev. A 77(1), 012504 (2008)]. FDET provides the expression for the embedding potential as a functional of the electron density of the embedded species, electron density of the environment, and the field generated by other charges in the environment. Under certain conditions, FDET leads to the exact ground-state energy and density of the whole system. Following Perdew-Levy theorem on stationary states of the ground-state energy functional, the other-than-ground-state stationary states of the FDET energy functional correspond to excited states. In the present work, we analyze such use of other-than-ground-state embedded wavefunctions obtained in practical calculations, i.e., when the FDET embedding potential is approximated. Three computational approaches based on FDET, that assure self-consistent excitation energy and embedded wavefunction dealing with the issue of orthogonality of embedded wavefunctions for different states in a different manner, are proposed and discussed.
Other than lowest-energy stationary embedded wave functions obtained in Frozen-Density Embedding Theory (FDET) [T. A. Wesolowski, Phys. Rev. A 77, 012504 (2008)] can be associated with electronic excited states but they can be mutually non-orthogonal. Although this does not violate any physical principles — embedded wave functions are only auxiliary objects used to obtain stationary densities — working with orthogonal functions has many practical advantages. In the present work, we show numerically that excitation energies obtained using conventional FDET calculations (allowing for non-orthogonality) can be obtained using embedded wave functions which are strictly orthogonal. The used method preserves the mathematical structure of FDET and self-consistency between energy, embedded wave function, and the embedding potential (they are connected through the Euler-Lagrange equations). The orthogonality is built-in through the linearization in the embedded density of the relevant components of the total energy functional. Moreover, we show formally that the differences between the expectation values of the embedded Hamiltonian are equal to the excitation energies, which is the exact result within linearized FDET. Linearized FDET is shown to be a robust approximation for a large class of reference densities.
Embedded Systems have traditionally been developed in a highly customized manner. The user interface hardware and software along with the interface to the embedded system are typically unique to the system for which they are built, resulting in extra cost to the system in terms of development time and maintenance effort. World Wide Web standards have been developed in the passed ten years with the goal of allowing servers and clients to intemperate seamlessly. The client and server systems can consist of differing hardware and software platforms but the World Wide Web standards allow them to interface without knowing about the details of system at the other end of the interface. Embedded Web Technology is the merging of Embedded Systems with the World Wide Web. Embedded Web Technology decreases the cost of developing and maintaining the user interface by allowing the user to interface to the embedded system through a web browser running on a standard personal computer. Embedded Web Technology can also be used to simplify an Embedded System's internal network.
A key element in the density functional embedding theory (DFET) is the embedding potential. We discuss two major issues related to the embedding potential: (1) its non-uniqueness and (2) the numerical difficulty for solving for it, especially for the spin-polarized systems. To resolve the first issue, we extend DFET to finite temperature: all quantities, such as the subsystem densities and the total system’s density, are calculated at a finite temperature. This is a physical extension since materials work at finite temperatures. We show that the embedding potential is strictly unique at T > 0. To resolve the second issue, we introduce an efficient iterative embedding potential solver. We discuss how to relax the magnetic moments in subsystems and how to equilibrate the chemical potentials across subsystems. The solver is robust and efficient for several non-trivial examples, in all of which good quality spin-polarized embedding potentials were obtained. We also demonstrate the solver on an extended periodic system: iron body-centered cubic (110) surface, which is related to the modeling of the heterogeneous catalysis involving iron, such as the Fischer-Tropsch and the Haber processes. This work would make it efficient and accurate to perform embedding simulations of some challenging material problems, such as the heterogeneous catalysis and the defects of complicated spin configurations in electronic materials.
Here, in this study, we introduce a novel approach to coupled-cluster Green's function (CCGF) embedding by seamlessly integrating conventional CCGF theory with the state-of-the-art sub-system embedding sub-algebras coupled cluster (SES-CC) formalism. This integration focuses primarily on delineating the characteristics of the sub-system and the corresponding segments of the Green's function, defined explicitly by active orbitals. Crucially, our work involves the adaptation of the SES-CC paradigm, addressing the left eigenvalue problem through a distinct form of Hamiltonian similarity transformation. This advancement not only facilitates a comprehensive representation of the interaction between the embedded sub-system and its surrounding environment but also paves the way for the quantum mechanical description of multiple embedded domains, particularly by employing the emergent quantum flow algorithms. Our theoretical underpinnings further set the stage for a generalization to multiple embedded sub-systems. This expansion holds significant promise for the exploration and application of non-equilibrium quantum systems, enhancing the understanding of system–environment interactions. In doing so, the research underscores the potential of SES-CC embedding within the realm of quantum computations and multi-scale simulations, promising a good balance between accuracy and computational efficiency.
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Olivine-structured LiMn1-xFexPO4 has become a promising candidate for cathode materials owing to its higher working voltage of 4.1 V and thus larger energy density than that of LiFePO4, which has been used for electric vehicles batteries with the advantage of high safety but disadvantage of low energy density due to its lower working voltage of 3.4 V. One drawback of LiMn1-xFexPO4 electrode is its relatively low electronic and Li-ionic conductivity with Li-ion one-dimensional diffusion. Herein, olivine-structured α-LiMn0.5Fe0.5PO4 nanocrystals were synthesized with optimized Li-ion diffusion channels in LiMn1-xFexPO4 nanocrystals by inducing high concentrations of Fe2+-Li+ antisite defects, which showed impressive capacity improvements of approaching 162, 127, 73, and 55 mAh g-1 at 0.1, 10, 50, and 100 C, respectively, and a long-term cycling stability of maintaining about 74% capacity after 1000 cycles at 10 C. By using high-resolution transmission electron microscopy imaging and joint refinement of hard X-ray and neutron powder diffraction patterns, we revealed that the extraordinary high-rate performance could be achieved by suppressing the formation of electrochemically inactive phase (β-LiMn1-xFexPO4, which is first reported in this work) embedded in α-LiMn0.5Fe0.5PO4. Because of the coherent orientation relationship between β- and α- phases, the β-phase embedded would impede the Li+ diffusion along the [100] and/or [001] directions that was activated by the high density of Fe2+-Li+ antisite (4.24%) in α-phase. Thus, by optimizing concentrations of Fe2+-Li+ antisite defects and suppressing β-phase-embedded olivine structure, Li-ion diffusion properties in LiMn1-xFexPO4 nanocrystals can be tuned by generating new Li+ tunneling. These findings may provide insights into the design and generation of other advanced electrode materials with improved rate performance.
We report a new implementation of the density functional embedding theory (DFET) in the VASP code, using the projector-augmented-wave (PAW) formalism. Newly developed algorithms allow us to efficiently perform optimized effective potential optimizations within PAW. The new algorithm generates robust and physically correct embedding potentials, as we verified using several test systems including a covalently bound molecule, a metal surface, and bulk semiconductors. We show that with the resulting embedding potential, embedded cluster models can reproduce the electronic structure of point defects in bulk semiconductors, thereby demonstrating the validity of DFET in semiconductors for the first time. Compared to our previous version, the new implementation of DFET within VASP affords use of all features of VASP (e.g., a systematic PAW library, a wide selection of functionals, a more flexible choice of U correction formalisms, and faster computational speed) with DFET. Furthermore, our results are fairly robust with respect to both plane-wave and Gaussian type orbital basis sets in the embedded cluster calculations. This suggests that the density functional embedding method is potentially an accurate and efficient way to study properties of isolated defects in semiconductors.
A computational technique has been developed to perform compressible flow simulations involving moving boundaries using an embedded boundary approach within the block-structured adaptive mesh refinement (SAMR) framework of AMReX [1], [91], [92]. We leverage the SAMR capability to obtain quantitatively accurate results whilst using robust, second-order finite volume schemes. A conservative, unsplit, cut-cell approach is utilized and a ghost-cell approach is developed for computing the flux on the moving, embedded boundary faces. A third-order least-squares formulation has been developed to compute the wall velocity gradients, and was found to significantly improve the performance of the solver in terms of the quantitative comparison of surface quantities such as the skin friction coefficient. Various test cases are performed to validate the method, and compared with analytical, experimental, and other numerical results in literature. Inviscid and viscous test cases are performed that span a wide regime of flow speeds - acoustic (harmonically pulsating sphere), smooth flows (expansion fan created by a receding piston) and flows with shocks (shock-cylinder interaction, shock-wedge interaction, pitching NACA 0012 airfoil and shock-cone interaction). A closed system with moving boundaries - an oscillating piston in a cylinder, showed that the percentage error in mass within the system decreases with refinement, demonstrating that the numerical scheme is conservative with grid refinement, but is not discretely conservative. Viscous test cases involve that of a horizontally moving cylinder at Re = 40, an inline oscillating cylinder at Re = 100, and a transversely oscillating cylinder at Re = 185. The judicious use of adaptive mesh refinement with appropriate refinement criteria to capture the regions of interest leads to well-resolved flow features, and good quantitative comparison is observed with the results available in literature.
Improving nuclear reactor power system designs and fuel-processing technologies for safer and more efficient operation requires the development of new component designs. In particular, many of the advanced reactor designs such as the molten salt reactors and high-temperature gas-cooled reactors have operating environments beyond the capability of most currently available commercial components. To address this gap, new cross-cutting technologies need to be developed that will enable design, fabrication, and reliable operation of new classes of reactor components. The Advanced Sensor Initiative of the Nuclear Energy Enabling Technologies initiative is investigating advanced sensor and control designs that are capable of operating in these extreme environments. Under this initiative, Oak Ridge National Laboratory (ORNL) has been developing embedded instrumentation and control (I&C) for extreme environments. To develop, test, and validate these new sensing and control techniques, ORNL is building a pump test bed that utilizes submerged magnetic bearings to levitate the shaft. The eventual goal is to apply these techniques to a high-temperature (700°C) canned rotor pump that utilizes active magnetic bearings to eliminate the need for mechanical bearings and seals. The technologies will benefit the Next Generation Power Plant, Advanced Reactor Concepts, and Small Modular Reactor programs. In this paper, we will detail the design and analysis of the embedded I&C test bed with submerged magnetic bearings, focusing on the interplay between the different major systems. Then we will analyze the forces on the shaft and their role in the magnetic bearing design. Next, we will develop the radial and thrust bearing geometries needed to meet the operational requirements of the test bed. In conclusion, we will present some initial system identification results to validate the theoretical models of the test bed dynamics.
Highlights: • Typical Medium Theory (TMT) for the Anderson Localization. • Locally Self-Consistent Multiple Scattering Method (LSMS) for Random Disordered Systems. • Linear Scaling Computational Method for Random Disordered Systems. We apply the recently developed embedding scheme for the locally self-consistent method to random disorder electrons systems. The method is based on the locally self-consistent multiple scattering theory and the typical medium theory. The locally self-consistent multiple scattering theory divides a system into many small designated local interaction zones. The subsystem within each local interaction zone is embedded in a self-consistent field from the typical medium theory. This approximation allows the study of random systems with large numbers of sites. We present results for the three dimensional Anderson model with different random disorder potential distributions. Using the typical density of states as an indicator of Anderson localization, we find that the method can capture the localization for commonly studied disorder potentials. These include the uniform distribution, the Gaussian distribution, and even the unbounded Cauchy distribution.
Because of the confinement effect, Ni embedded on the interior surface of BNNT exhibits a much higher catalytic activity for CO oxidation by comparing with that embedded in h-BN or on the outside surface of BNNT.