Analytical Derivatives Research Articles

Exploring differential geometry in neural implicits

We introduce a neural implicit framework that exploits the differentiable properties of neural networks and the discrete geometry of point-sampled surfaces to approximate them as the level sets of neural implicit functions.To train a neural implicit function, we propose a loss functional that approximates a signed distance function, and allows terms with high-order derivatives, such as the alignment between the principal directions of curvature, to learn more geometric details. During training, we consider a non-uniform sampling strategy based on the curvatures of the point-sampled surface to prioritize points with more geometric details. This sampling implies faster learning while preserving geometric accuracy when compared with previous approaches.We also use the analytical derivatives of a neural implicit function to estimate the differential measures of the underlying point-sampled surface.

Reformulation of expressions for thermoelastic properties of crystals using harmonic mapping

We introduce alternative statistical mechanics expressions for thermoelastic properties of monatomic crystals, in the classical canonical ensemble, using the mapped-averaging framework. Two reference models are examined as a basis for the coordinate mapping: ideal gas (IG) and quasiharmonic (QH) crystal. The former prescribes affine mapping and yields the conventional expressions for the properties, while the latter has, in addition, a nonaffine component and yields alternative formulas, denoted as ``harmonically mapped averaging'' (HMA). While the (strain-dependent) affine mapping is universal and does not require system-specific information, the HMA method uses a system- (and strain-) dependent matrix to specify the mapping; this mapping matrix is derived from the force-constant matrix of the QH reference and its variation with strains. In practice, implementing HMA (and affine) schemes depends on the availability of analytical (Lagrangian) derivatives of potential energy with respect to strains (e.g., Born term) and coordinates (e.g., Hessian matrix); however, when these derivatives are unavailable, numerical derivatives can be computed via finite difference by moving atoms according the mapping matrices, while straining the crystal. In no circumstances does mapped averaging affect sampling of configurations; it involves only the calculation of averages. Results show that HMA provides CPU speedup ranging from a factor of 4, to over two orders of magnitude, compared to the conventional formulas (especially at low temperature), with the best performance obtained with isotropic-deformation properties. In addition to its application to molecular simulation, HMA can open avenues for developing new theories for thermoelastic properties.

Analytic Light Curve for Mutual Transits of Two Bodies Across a Limb-darkened Star

We present a solution for the light curve of two bodies mutually transiting a star with polynomial limb darkening. The term “mutual transit” in this work refers to a transit of the star during which overlap occurs between the two transiting bodies. These could be an exoplanet with an exomoon companion, two exoplanets, an eclipsing binary and a planet, or two stars eclipsing a third in a triple-star system. We include analytic derivatives of the light curve with respect to the positions and radii of both bodies. We provide code that implements a photodynamical model for a mutual transit. We include two dynamical models, one for hierarchical systems in which a secondary body orbits a larger primary (e.g., an exomoon system) and a second for confocal systems in which two bodies independently orbit a central mass (e.g., two planets in widely separated orbits). Our code is fast enough to enable inference with Markov Chain Monte Carlo algorithms, and the inclusion of derivatives allows for the use of gradient-based inference methods such as Hamiltonian Monte Carlo. While applicable to a variety of systems, this work was undertaken primarily with exomoons in mind. It is our hope that making this code publicly available will reduce barriers for the community to assess the detectability of exomoons, conduct searches for exomoons, and attempt to validate existing exomoon candidates. We also anticipate that our code will be useful for studies of planet–planet transits in exoplanetary systems, transits of circumbinary planets, and eclipses in triple-star systems.

Adaptive construction of shallower quantum circuits with quantum spin projection for fermionic systems

Quantum computing is a promising approach to harnessing strong correlation in molecular systems; however, current devices only allow for hybrid quantum-classical algorithms with a shallow circuit depth, such as the variational quantum eigensolver (VQE). In this paper, we report the importance of the Hamiltonian symmetry in constructing VQE circuits adaptively (ADAPT-VQE). This treatment often violates symmetry, thereby deteriorating the convergence of fidelity to the exact solution and ultimately resulting in deeper circuits. We demonstrate that spin-symmetry projection can provide a simple yet effective solution to this problem, by keeping the quantum state in the correct symmetry space, to reduce the overall gate operations. To further investigate the role of spin-symmetry in computing molecular properties with ADAPT-VQE, we have derived the analytical derivative of symmetry-projected VQE energy. Our illustrative calculations reveal the significance of preserving symmetry in providing accurate dipole moments and geometries with variational approximations.

Concurrent Trajectory Optimization and Aircraft Design for the Air Cargo Challenge Competition

A coupled aerostructural aircraft design and trajectory optimization framework is developed for the Air Cargo Challenge competition to maximize the expected score based on cargo carried, altitude achieved and distance traveled. Its modular architecture makes it easily adaptable to any problem where the performance depends not only on the design of the aircraft but also on its flight trajectory. It is based on OpenAeroStruct, an aerostructural solver that uses analytic derivatives for efficient gradient-based optimization. A trajectory optimization module using a collocation method is coupled with the option of using b-splines to increase computational efficiency together with an experimentally-based power decay model that accurately determines the aircraft propulsive response to control input depending on the battery discharge level. The optimization problem totaled 206 variables and 283 constraints and was solved in less than 7 h on a standard computer with 12% reduction when using b-splines for trajectory control variables. The results revealed the need to consider the multi-objective total score to account for the different score components and highlighted the importance of the payload level and chosen trajectory. The wing area should be increased within allowable limits to maximize payload capacity, climb to maximum target height should be the focus of the first 60 s of flight and full throttle should be avoided in cruise to reduce losses and extend flight distance. The framework proved to be a valuable tool for students to easily obtain guidelines for both the model aircraft design and control to maximize the competition score.

Direct computation of magnetic surfaces in Boozer coordinates and coil optimization for quasisymmetry

We propose a new method to compute magnetic surfaces that are parametrized in Boozer coordinates for vacuum magnetic fields. We also propose a measure for quasisymmetry on the computed surfaces and use it to design coils that generate a magnetic field that is quasisymmetric on those surfaces. The rotational transform of the field and complexity measures for the coils are also controlled in the design problem. Using an adjoint approach, we are able to obtain analytic derivatives for this optimization problem, yielding an efficient gradient-based algorithm. Starting from an initial coil set that presents nested magnetic surfaces for a large fraction of the volume, our method converges rapidly to coil systems generating fields with excellent quasisymmetry and low particle losses. In particular for low complexity coils, we are able to significantly improve the performance compared with coils obtained from the standard two-stage approach, e.g. reduce losses of fusion-produced alpha particles born at half-radius from $17.7\,\%$ to $6.6\,\%$ . We also demonstrate 16-coil configurations with alpha loss ${<}1\,\%$ and neoclassical transport magnitude $\epsilon _{\text {eff}}^{3/2}$ less than approximately $5\times 10^{-9}$ .

المنهج الدعوي في الحوار النبويﷺ ودروسه الدعوية مع ذكر بعض النماذج من الصحيحين

Dialogue has been always a very effective way of communication amongst human beings and communities. The Holy Prophets have been traditionally using the same, as the source and means to preach God’s message. The Holy Prophet Muhammad (PBUH) had been extraordinary and matchless in this regard as well. The following study focuses on lessons from the Holy Prophet's Dialogues as an Analytic Derivative Study (selected Aḥadīth from Bukhāri and Muslim) to learn how The Holy Prophet (PBUH) communicated in various roles as an individual, such as orator, leader, husband, merchant, president of the state, preacher, and as a family member. The analysis will derive etiquette for preaching from the Sīrah of the Holy Prophet (PBUH).

Generalized Bezier components and successive component refinement using moving morphable components

This paper demonstrates developments that introduce generalized Bezier components in the Moving Morphable Components (MMC) optimization framework. Methods of enhancing the parameterization of the components to provide the opportunity for a better optimum, than can be achieved using existing approaches, are also described. The use of control points and Bezier curves for representing structural components provides both additional flexibility in the shape and a parameterization that complies with extrude and swept feature-based templates available in commercial computer-aided design (CAD) packages. Methods of representing these structural components, calculating analytical derivatives, and numerical examples demonstrating their integration in the MMC framework, are presented for a series of author-derived and literature problems. A successive refinement technique demonstrates how the additional flexibility in the structural components enables progressive improvement in the objective function. For the examined problems, increasing the design variables per component (from 5 to 15) resulted in solutions with 6% to 36% reduction in compliance. This improvement was achieved without increasing the number of components in the design space.

Neural network QCD analysis of charged hadron fragmentation functions in the presence of SIDIS data

In this paper, we present a QCD analysis to extract the Fragmentation Functions (FFs) of unidentified light charged hadron entitled as SHK22.h from high-energy lepton-lepton annihilation and lepton-hadron scattering data sets. This analysis includes the data from all available single inclusive electron-positron annihilation (SIA) processes and semi-inclusive deep-inelastic scattering (SIDIS) measurements for the unidentified light charged hadron productions. The SIDIS data which has been measured by the COMPASS experiment could allow the flavor dependence of the FFs to be well constrained. We exploit the analytic derivative of the Neural Network (NN) for fitting of FFs at next-to-leading-order (NLO) accuracy in the perturbative QCD (pQCD). The Monte Carlo method is implied for all sources of experimental uncertainties and the Parton distribution functions (PDFs) as well. Very good agreements are achieved between the SHK22.h FFs set and the most recent QCD fits available in literature, namely JAM20 and NNFF1.1h. In addition, we discuss the impact arising from the inclusion of SIDIS data on the extracted light-charged hadron FFs. The global QCD resulting at NLO for charged hadron FFs provides valuable insights for applications in present and future high-energy measurement of charged hadron final state processes.

A novel prescribed performance controller for strict-feedback nonlinear systems with input constraints

This paper is devoted to the prescribed performance control (PPC) for a class of strict-feedback nonlinear systems with input saturation constraints. With the help of an improved tuning function, the system can achieve the desired steady-state and transient performance in the pre-designed time. A new error transformation function is introduced, which has inherent robustness, so it does not need to use any approximation technique or calculate the analytical derivative. Compared with the relevant results, the proposed scheme has the same lower complexity, but better transient and steady-state performance, although there exists uncertain nonlinearity and uncertain disturbances in the system. Finally, the correctness of the above algorithm is verified by simulation experiments.

Analytic First-Order Derivatives of (X)MS, XDW, and RMS Variants of the CASPT2 and RASPT2 Methods.

Crossings between states involve complex electronic structures, making the accurate characterization of the crossing point difficult. In this study, the analytic derivatives of three complete active space second-order perturbation theory (CASPT2) variants as well as an extension of the restricted active space (RASPT2) are developed. These variants are applied to locating minimum energy conical intersections. Our results demonstrate that the three CASPT2 variants predict qualitatively similar results, but a recently developed variant, the rotated multistate CASPT2 (RMS-CASPT2), is least sensitive to the number of states considered in the calculation. We demonstrate that CASPT2 and the reference self-consistent field calculations predict qualitatively different energetics and bond lengths.

Reaction Entropies in Solution from Analytical Three-Dimensional Reference Interaction Site Model Derivatives with Application to Redox and Spin-Crossover Processes.

An analytical approach to compute the excess entropy of solvation at constant pressure in three-dimensional reference interaction site model (3D-RISM) calculations is presented. It includes the changes in the macroscopic dielectric constant of the solvent upon variation of temperature and density. The approach is exact within the framework of force-field descriptions of the solute and gives reasonable results for self-consistently determined electrostatics as used in the 3D-RISM-self-consistent field approach, particularly for entropy differences. The new method is applied to simple examples of reaction entropies of iron complexes in aqueous solution, for which simple gas-phase calculations and many other approaches give unreliable estimates. For both redox half-reactions and spin-crossover processes, (semi)quantitative agreement with experimental reaction entropies can be achieved out of the box.

Evaluation of molecular photophysical and photochemical properties using linear response time-dependent density functional theory with classical embedding: Successes and challenges.

Time-dependent density functional theory (TDDFT) based approaches have been developed in recent years to model the excited-state properties and transition processes of the molecules in the gas-phase and in a condensed medium, such as in a solution and protein microenvironment or near semiconductor and metal surfaces. In the latter case, usually, classical embedding models have been adopted to account for the molecular environmental effects, leading to the multi-scale approaches of TDDFT/polarizable continuum model (PCM) and TDDFT/molecular mechanics (MM), where a molecular system of interest is designated as the quantum mechanical region and treated with TDDFT, while the environment is usually described using either a PCM or (non-polarizable or polarizable) MM force fields. In this Perspective, we briefly review these TDDFT-related multi-scale models with a specific emphasis on the implementation of analytical energy derivatives, such as the energy gradient and Hessian, the nonadiabatic coupling, the spin-orbit coupling, and the transition dipole moment as well as their nuclear derivatives for various radiative and radiativeless transition processes among electronic states. Three variations of the TDDFT method, the Tamm-Dancoff approximation to TDDFT, spin-flip DFT, and spin-adiabatic TDDFT, are discussed. Moreover, using a model system (pyridine-Ag20 complex), we emphasize that caution is needed to properly account for system-environment interactions within the TDDFT/MM models. Specifically, one should appropriately damp the electrostatic embedding potential from MM atoms and carefully tune the van der Waals interaction potential between the system and the environment. We also highlight the lack of proper treatment of charge transfer between the quantum mechanics and MM regions as well as the need for accelerated TDDFT modelings and interpretability, which calls for new method developments.

Recent developments on graphene and its derivatives based electrochemical sensors for determinations of food contaminants

The sensing of food contaminants is essential to prevent their adverse health effects on the consumers. Electrochemical sensors are promising in the determination of electroactive analytes including food pollutants, biomolecules etc. Graphene nanomaterials offer many benefits as electrode material in a sensing device. To further improve the analytical performance, doped graphene or derivatives of graphene such as reduced graphene oxide and their nanocomposites were explored as electrode materials. Herein, the advancements in graphene and its derivatives-based electrochemical sensors for analysis of food pollutants were summarized. Determinations of both organic (food colourants, pesticides, drugs, etc.) and inorganic pollutants (metal cations and anions) were considered. The influencing factors including nature of electrode materials and food pollutants, pH, electroactive surface area etc., on the sensing performances of modified electrodes were highlighted. The results of pollutant detection in food samples by the graphene-based electrode have also been outlined. Lastly, conclusions and current challenges in effective real sample detection were presented.

Single-stage gradient-based stellarator coil design: stochastic optimization

We extend the single-stage stellarator coil design approach for quasi-symmetry on axis from (Giuliani et al 2020) to additionally take into account coil manufacturing errors. By modeling coil errors independently from the coil discretization, we have the flexibility to consider realistic forms of coil errors. The corresponding stochastic optimization problems are formulated using a risk-neutral approach and risk-averse approaches. We present an efficient, gradient-based descent algorithm which relies on analytical derivatives to solve these problems. In a comprehensive numerical study, we compare the coil designs resulting from deterministic and risk-neutral stochastic optimization and find that the risk-neutral formulation results in more robust configurations and reduces the number of local minima of the optimization problem. We also compare deterministic and risk-neutral approaches in terms of quasi-symmetry on and away from the magnetic axis, and in terms of the confinement of particles released close to the axis. Finally, we show that for the optimization problems we consider, a risk-averse objective using the conditional value-at-risk leads to results which are similar to the risk-neutral objective.

Accurate Prediction of Nuclear Magnetic Resonance Parameters via the XYG3 Type of Doubly Hybrid Density Functionals.

Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful and versatile tools in elucidating molecular structures. To eliminate ambiguities of experimental assignments, accurate calculations of NMR spectra are of great importance. Here, a method for theoretical evaluation of the NMR shielding constants by analytic derivatives using gauge including atomic orbitals (GIAO) has been implemented for the XYG3 type of doubly hybrid density functionals (xDH), namely, the GIAO-xDH method. Benchmark calculations on shielding constants and chemical shifts demonstrate the remarkable accuracy of the GIAO-xDH method, compared to the accurate CCSD(T) references. It is shown here that the XYGJ-OS functional is able to give a mean absolute deviation (MAD) of ∼3.0 ppm in the calculated shielding constants for 13C, 15N, 17O, 19F, while both XYGJ-OS and xDH-PBE0 functionals are able to provide a satisfactory estimation of chemical shifts with MADs of ∼0.03 and 1.0 ppm for 1H and 13C, respectively. The basis set influence upon the method has been examined and a computational scheme considering both accuracy and efficiency has been proposed and tested to predict the experimental 13C chemical shifts of five medium-sized natural product molecules, yielding a MAD of ∼1.0 ppm, which demonstrates the practical feasibility of the GIAO-xDH method.

Impact of Using Analytic Derivatives In Optimization For N-Impulse Orbit Transfer Problems

Impact of Using Analytic Derivatives In Optimization For N-Impulse Orbit Transfer Problems

Quasi-Relativistic Calculation of EPR g Tensors with Derivatives of the Decoupling Transformation, Gauge-Including Atomic Orbitals, and Magnetic Balance

We present an exact two-component (X2C) ansatz for the EPR g tensor using gauge-including atomic orbitals (GIAOs) and a magnetically balanced basis set expansion. In contrast to previous X2C and four-component relativistic ansätze for the g tensor, this implementation results in a gauge-origin-invariant formalism. Furthermore, the derivatives of the relativistic decoupling matrix are incorporated to form the complete analytical derivative of the X2C Hamiltonian. To reduce the associated computational costs, we apply the diagonal local approximation to the unitary decoupling transformation (DLU). The quasi-relativistic X2C and DLU-X2C Hamiltonians accurately reproduce the results of the parent four-component relativistic theory when accounting for two-electron picture-change effects with the modified screened nuclear spin-orbit approximation in the respective one-electron integrals and integral derivatives. According to our benchmark studies, the uncontracted Dyall and segmented-contracted Karlsruhe x2c-type basis sets perform well when compared to large even-tempered basis sets. Moreover, (range-separated) hybrid density functional approximations such as LC-ωPBE and ωB97X-D are needed to match the experimental findings. The impact of the GIAOs depends on the distribution of the spin density, and their use may change the Δg shifts by 10-50% as shown for [(C5Me5)2Y(μ-S)2Mo(μ-S)2Y(C5Me5)2]-. Routine calculations of large molecules are possible with widely available and comparably low-cost hardware as demonstrated for [Pt(C6Cl5)4]- with 3003 basis functions and three spin-(1/2) La(II) and Lu(II) compounds, for which we observe good agreement with the experimental findings.

RTGW2020: An efficient implementation of the multi-orbital Gutzwiller method with general local interactions

In the present paper, we propose an efficient numerical scheme for Gutzwiller method for multi-band Hubbard models with general onsite Coulomb interaction. Following the basic idea of Deng et al. (2009) [26] and extensions by Lanata et al. (2012) [28], the ground state is variationally determined through optimizing the total energy with respect to the variational single particle density matrix (n0), which is called “outer loop”. In the corresponding “inner loop” where n0 is fixed, the non-interacting wave function and the parameters contained in the Gutzwiller projector are determined by a two-step iterative approach. All derivatives of the implementation process have been analytically derived, which allows us to apply some advanced minimization or root-searching algorithms for both the inner and outer loops leading to the highly efficient convergence. In addition, an atomic diagonalization method taking the point group symmetry into account has been developed for the customized design of the Gutzwiller projector, making it convenient to explore many interesting orders at a lower cost of computation. As benchmarks, several different types of correlated models have been studied utilizing the proposed method, which are in perfect agreement with the previous results by DMFT and multi-orbital slave-boson mean field method. Compared with the linear mixing method, Newton's method with analytical derivatives shows much faster convergence for the inner loop. As for the outer loop, the minimization using analytical derivatives also shows much better stability and efficiency compared with that using numerical derivatives.

Analytic derivatives of scaling motion-compensated projection operators for dynamic computed tomography

This work presents an iterative method in dynamic computed tomography (dynamic CT) which enables accurate reconstruction and scaling correction simultaneously. Recent achievements in dynamic CT have shown the feasibility of reconstructing an image of an object while simultaneously estimating its temporal deformation model parameters. Those approaches consider a mathematical minimization problem whose objective function depends on the reconstruction as well as on the deformation parameters corresponding to the motion model, mostly represented by affine transformations. Nonetheless, they lack a validation on non-rigid motion models wherein the motion is estimated by an objective function whose partial derivatives toward motion parameters are analytical. In this work, those operators are given exactly. Results from simulation and real data experiments show that the blurring artifacts caused by the motion are largely reduced.