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DOE OSTI2024

Projective Integral Updates for High-Dimensional Variational Inference

Variational inference is an approximation framework for Bayesian inference that seeks to improve quantified uncertainty in predictions by optimizing a simplified distribution over parameters to stand in for the full posterior. Capturing model variations that remain consistent with training data enables more robust predictions by reducing parameter sensitivity. This work introduces a fixed-point optimization for variational inference that is applicable when every feasible log density can be expressed as a linear combination of functions from a given basis. In such cases, the optimizer becomes a fixed-point of projective integral updates. When the basis spans univariate quadratics in each parameter, the feasible distributions are Gaussian mean-fields and the projective integral updates yield quasi-Newton variational Bayes (QNVB). Other bases and updates are also possible. Since these updates require high-dimensional integration, this work begins by proposing an efficient quasirandom sequence of quadratures for mean-field distributions. Each iterate of the sequence contains two evaluation points that combine to correctly integrate all univariate quadratic functions and, if the mean-field factors are symmetric, all univariate cubics. More importantly, averaging results over short subsequences achieves periodic exactness on a much larger space of multivariate polynomials of quadratic total degree. The corresponding variational updates require four loss evaluations with standard (not second-order) backpropagation to eliminate error terms from over half of all multivariate quadratic basis functions. Furthermore, this integration technique is motivated by first proposing stochastic blocked mean-field quadratures, which may be useful in other contexts. A PyTorch implementation of QNVB allows for better control over model uncertainty during training than competing methods. Experiments demonstrate superior generalizability for multiple learning problems and architectures.

Gaussian mean-field↗

DOE OSTI2014

Harmonics in the dark-matter sky: directional detection in the Fourier-Bessel basis

Details about the velocity distribution of weakly interacting massive particle (WIMP) dark matter in our galaxy may be revealed by nuclear-recoil detectors with directional sensitivity. Previous studies have assumed that the velocity distribution takes a simple functional form characterized by a small number of parameters. More recent work has shown that basis-function expansions may allow for more general parameterization; such an approach has been considered for both the one-dimensional speed and momentum distributions, and also for three-dimensional velocity distributions obeying certain equilibrium conditions. In this work, I extend this basis-function approach to allow for arbitrary velocity distributions by working in the Fourier-Bessel basis, deriving an analytic expression for the directional recoil spectrum. Such an approach is completely general, and may be useful if the velocity distribution is too complex to be characterized by simple functional forms or is not completely virialized. Results concerning the three-dimensional Radon transform of the Fourier-Bessel basis functions may be of general interest for tomographic applications.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration

This article is concerned with the approximation of high-dimensional functions by kernel-based methods. Motivated by uncertainty quantification, which often necessitates the construction of approximations that are accurate with respect to a probability density function of random variables, we aim at minimizing the approximation error with respect to a weighted $L^p$-norm. We present a greedy procedure for designing computer experiments based upon a weighted modification of the pivoted Cholesky factorization. The method successively generates nested samples with the goal of minimizing error in regions of high probability. Numerical experiments validate that this new importance sampling strategy is superior to other sampling approaches, especially when used with non-product probability density functions. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Multisliced gausslet basis sets for electronic structure

We introduce highly local basis sets for electronic structure which are very efficient for correlation calculations near the complete basis set limit. Our approach is based on gausslets, recently introduced waveletlike smooth orthogonal functions. We adapt the gausslets to particular systems using one-dimensional (1D) coordinate transformations, putting more basis functions near the nuclei, while maintaining orthogonality. Furthermore, three-dimensional basis functions are composed out of products of the 1D functions in an efficient way called multislicing. We demonstrate these bases with both Hartree-Fock and density matrix renormalization group calculations on hydrogen chain systems. With both methods, we can go to higher accuracy in the complete basis set limit than is practical for conventional Gaussian basis sets, with errors near 0.1 mH per atom.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Understanding, inhibiting, and engineering membrane transporters with high-throughput mutational screens

Promiscuous membrane transporters play vital roles across domains of life, mediating the uptake and efflux of structurally and chemically diverse substrates. Although many transporter structures have been solved, the fundamental rules of polyspecific transport remain inscrutable. In recent years, high-throughput genetic screens have solidified as powerful tools for comprehensive, unbiased measurements of variant function and hypothesis generation, but have had infrequent application and limited impact in the transporter field. In this primer, we describe the principles of high-throughput screening methods available for studying polyspecific transporters and comment on the necessity and potential of high-throughput methods for deciphering these transporters in particular. We present several screening approaches which could provide a fundamental understanding of the molecular basis of function and promiscuity in transporters. Here, we further posit how this knowledge can be leveraged to design inhibitors that combat multidrug resistance and engineer transporters as needed tools for synthetic biology and biotechnology applications.

EPIs↗

DOE OSTI2019

Spin-State Ordering in Metal-Based Compounds Using the Localized Active Space Self-Consistent Field Method

Quantitatively accurate calculations for spin-state ordering in transition-metal complexes typically demand a robust multiconfigurational treatment. The poor scaling of such methods with increasing size makes them impractical for large, strongly correlated systems. Density matrix embedding theory (DMET) is a fragmentation approach that can be used to specifically address this challenge. The single-determinantal bath framework of DMET is applicable in many situations, but it has been shown to perform poorly for molecules characterized by strong correlation when a multiconfigurational self-consistent field solver is used. To ameliorate this problem, the localized active space self-consistent field (LASSCF) method was recently described. Here in this work, LASSCF is applied to predict spin-state energetics in mono- and di-iron systems, and we show that the model offers an accuracy equivalent to that of CASSCF but at a substantially lower computational cost. Performance as a function of basis set and active space is also examined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Energetic Basis and Design of Enzyme Function Demonstrated Using GFP, an Excited-State Enzyme

We report the past decades have witnessed an explosion of de novo protein designs with a remarkable range of scaffolds. It remains challenging, however, to design catalytic functions that are competitive with naturally occurring counterparts as well as biomimetic or nonbiological catalysts. Although directed evolution often offers efficient solutions, the fitness landscape remains opaque. Green fluorescent protein (GFP), which has revolutionized biological imaging and assays, is one of the most redesigned proteins. While not an enzyme in the conventional sense, GFPs feature competing excited-state decay pathways with the same steric and electrostatic origins as conventional ground-state catalysts, and they exert exquisite control over multiple reaction outcomes through the same principles. Thus, GFP is an “excited-state enzyme”. Herein we show that rationally designed mutants and hybrids that contain environmental mutations and substituted chromophores provide the basis for a quantitative model and prediction that describes the influence of sterics and electrostatics on excited-state catalysis of GFPs. As both perturbations can selectively bias photoisomerization pathways, GFPs with fluorescence quantum yields (FQYs) and photoswitching characteristics tailored for specific applications could be predicted and then demonstrated. The underlying energetic landscape, readily accessible via spectroscopy for GFPs, offers an important missing link in the design of protein function that is generalizable to catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1991

Light scattering by lunar-like particle size distributions

A fundamental input to models of light scattering from planetary regoliths is the mean phase function of the regolith particles. Using the known size distribution for typical lunar soils, the mean phase function and mean linear polarization for a regolith volume element of spherical particles of any composition were calculated from Mie theory. The two contour plots given here summarize the changes in the mean phase function and linear polarization with changes in the real part of the complex index of refraction, n - ik, for k equals 0.01, the visible wavelength 0.55 micrometers, and the particle size distribution of the typical mature lunar soil 72141. A second figure is a similar index-phase surface, except with k equals 0.1. The index-phase surfaces from this survey are a first order description of scattering by lunar-like regoliths of spherical particles of arbitrary composition. They form the basis of functions that span a large range of parameter-space.

Goguen, Jay D.↗

DOE OSTI2017

Cytotoxic protein from the mushroom Coprinus comatus possesses a unique mode for glycan binding and specificity

Glycans possess significant chemical diversity; glycan binding proteins (GBPs) recognize specific glycans to translate their structures to functions in various physiological and pathological processes. Therefore, the discovery and characterization of novel GBPs and characterization of glycan–GBP interactions are significant to provide potential targets for therapeutic intervention of many diseases. Here, we report the biochemical, functional, and structural characterization of a 130-amino-acid protein, Y3, from the mushroom Coprinus comatus . Biochemical studies of recombinant Y3 from a yeast expression system demonstrated the protein is a unique GBP. Additionally, we show that Y3 exhibits selective and potent cytotoxicity toward human T-cell leukemia Jurkat cells compared with a panel of cancer cell lines via inducing caspase-dependent apoptosis. Screening of a glycan array demonstrated GalNAcβ1–4(Fucα1–3)GlcNAc (LDNF) as a specific Y3-binding ligand. Here, to provide a structural basis for function, the crystal structure was solved to a resolution of 1.2 Å, revealing a single-domain αβα-sandwich motif. Two monomers were dimerized to form a large 10-stranded, antiparallel β-sheet flanked by α-helices on each side, representing a unique oligomerization mode among GBPs. A large glycan binding pocket extends into the dimeric interface, and docking of LDNF identified key residues for glycan interactions. Disruption of residues predicted to be involved in LDNF/Y3 interactions resulted in the significant loss of binding to Jurkat T-cells and severely impaired their cytotoxicity. Collectively, these results demonstrate Y3 to be a GBP with selective cytotoxicity toward human T-cell leukemia cells and indicate its potential use in cancer diagnosis and treatment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Fast and anisotropic flexibility-rigidity index for protein flexibility and fluctuation analysis

Protein structural fluctuation, typically measured by Debye-Waller factors, or B-factors, is a manifestation of protein flexibility, which strongly correlates to protein function. The flexibility-rigidity index (FRI) is a newly proposed method for the construction of atomic rigidity functions required in the theory of continuum elasticity with atomic rigidity, which is a new multiscale formalism for describing excessively large biomolecular systems. The FRI method analyzes protein rigidity and flexibility and is capable of predicting protein B-factors without resorting to matrix diagonalization. A fundamental assumption used in the FRI is that protein structures are uniquely determined by various internal and external interactions, while the protein functions, such as stability and flexibility, are solely determined by the structure. As such, one can predict protein flexibility without resorting to the protein interaction Hamiltonian. Consequently, bypassing the matrix diagonalization, the original FRI has a computational complexity of O(N{sup 2}). This work introduces a fast FRI (fFRI) algorithm for the flexibility analysis of large macromolecules. The proposed fFRI further reduces the computational complexity to O(N). Additionally, we propose anisotropic FRI (aFRI) algorithms for the analysis of protein collective dynamics. The aFRI algorithms permit adaptive Hessian matrices, from a completely global 3N × 3N matrix to completely local 3 × 3 matrices. These 3 × 3 matrices, despite being calculated locally, also contain non-local correlation information. Eigenvectors obtained from the proposed aFRI algorithms are able to demonstrate collective motions. Moreover, we investigate the performance of FRI by employing four families of radial basis correlation functions. Both parameter optimized and parameter-free FRI methods are explored. Furthermore, we compare the accuracy and efficiency of FRI with some established approaches to flexibility analysis, namely, normal mode analysis and Gaussian network model (GNM). The accuracy of the FRI method is tested using four sets of proteins, three sets of relatively small-, medium-, and large-sized structures and an extended set of 365 proteins. A fifth set of proteins is used to compare the efficiency of the FRI, fFRI, aFRI, and GNM methods. Intensive validation and comparison indicate that the FRI, particularly the fFRI, is orders of magnitude more efficient and about 10% more accurate overall than some of the most popular methods in the field. The proposed fFRI is able to predict B-factors for α-carbons of the HIV virus capsid (313 236 residues) in less than 30 seconds on a single processor using only one core. Finally, we demonstrate the application of FRI and aFRI to protein domain analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

A variational treatment of material configurations with application to interface motion and microstructural evolution

We present a unified variational treatment of evolving configurations in crystalline solids with microstructure. The crux of our treatment lies in the introduction of a vector configurational field. This field lies in the material, or configurational, manifold, in contrast with the traditional displacement field, which we regard as lying in the spatial manifold. We identify two distinct cases which describe (a) problems in which the configurational field's evolution is localized to a mathematically sharp interface, and (b) those in which the configurational field's evolution can extend throughout the volume. The first case is suitable for describing incoherent phase interfaces in polycrystalline solids, and the latter is useful for describing smooth changes in crystal structure and naturally incorporates coherent (diffuse) phase interfaces. These descriptions also lead to parameterizations of the free energies for the two cases, from which variational treatments can be developed and equilibrium conditions obtained. For sharp interfaces that are out-of-equilibrium, the second law of thermodynamics furnishes restrictions on the kinetic law for the interface velocity. The class of problems in which the material undergoes configurational changes between distinct, stable crystal structures are characterized by free energy density functions that are non-convex with respect to configurational strain. For physically meaningful solutions and mathematical well-posedness, it becomes necessary to incorporate interfacial energy. This we have done by introducing a configurational strain gradient dependence in the free energy density function following ideas laid out by Toupin (Arch. Rat. Mech. Anal., 11, 1962, 385-414). The variational treatment leads to a system of partial differential equations governing the configuration that is coupled with the traditional equations of nonlinear elasticity. The coupled system of equations governs the configurational change in crystal structure, and elastic deformation driven by elastic, Eshelbian, and configurational stresses. As a result, numerical examples are presented to demonstrate interface motion as well as evolving microstructures of crystal structures.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Hydrogenated fullerenes in space: FT-IR spectra analysis

Fullerenes and hydrogenated fullerenes are found in circumstellar and interstellar environments. But the determination structures for the detected bands in the interstellar and circumstellar space are not completely understood so far. For that purpose, the aim of this article is to provide all possible infrared spectra for C{sub 20} and C{sub 60} fullerenes and their hydrogenated fullerenes. Density Functional theory (DFT) is applied using B3LYP exchange-functional with basis set 6–31G(d, p). The Fourier transform infrared spectroscopy (FT-IR) is found to be capable of distinguishing between fullerenes, mono hydrogenated fullerenes and fully hydrogenated fullerenes. In addition, deposition of one hydrogen atom outside the fully hydrogenated fullerenes is found to be distinguished by forming H{sub 2} molecule at peak around 4440 cm{sup −1}. However, deposition of one hydrogen atom inside the fully hydrogenated fullerenes cannot be distinguished. The obtained spectral structures are analyzed and are compared with available experimental results.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Band and Impurity States in Dimond with the (MV){sup –} (M = Si, Ge, Sn) Centers Based on ab Initio Calculations

Ab initio calculations were used to determine the structure, electronic zone dispersion, partial (atomic and orbital) densities of states, and levels of impurity–vacancy cluster (MV){sup –} (M = Si, Ge, Sn) in diamond. Wavefunctions were determined in the density functional theory approximation using hybrid exchange-correlation functional, localized basis set of Gaussians, and a supercell consisting of 192 atoms. Results are compared with data on undoped diamond and with experimental results. The dependence of the direct and indirect energy gap on the cluster type is discussed. Calculations are carried out taking into account the spin–orbit interaction to identify impurity levels in the valence band. The effect of electron–electron interaction on the position of impurity levels is discussed. The dominant contribution of the d orbitals to the density of states at the impurity level in all clusters is detected.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

TurboRVB: A many-body toolkit for ab initio electronic simulations by quantum Monte Carlo

TurboRVB is a computational package for ab initio Quantum Monte Carlo (QMC) simulations of both molecular and bulk electronic systems. The code implements two types of well established QMC algorithms: Variational Monte Carlo (VMC) and diffusion Monte Carlo in its robust and efficient lattice regularized variant. A key feature of the code is the possibility of using strongly correlated many-body wave functions (WFs), capable of describing several materials with very high accuracy, even when standard mean-field approaches [e.g., density functional theory (DFT)] fail. The electronic WF is obtained by applying a Jastrow factor, which takes into account dynamical correlations, to the most general mean-field ground state, written either as an antisymmetrized geminal power with spin-singlet pairing or as a Pfaffian, including both singlet and triplet correlations. This WF can be viewed as an efficient implementation of the so-called resonating valence bond (RVB) Ansatz, first proposed by Pauling and Anderson in quantum chemistry [L. Pauling, The Nature of the Chemical Bond (Cornell University Press, 1960)] and condensed matter physics [P.W. Anderson, Mat. Res. Bull 8, 153 (1973)], respectively. The RVB Ansatz implemented in TurboRVB has a large variational freedom, including the Jastrow correlated Slater determinant as its simplest, but nontrivial case. Moreover, it has the remarkable advantage of remaining with an affordable computational cost, proportional to the one spent for the evaluation of a single Slater determinant. Therefore, its application to large systems is computationally feasible. The WF is expanded in a localized basis set. Several basis set functions are implemented, such as Gaussian, Slater, and mixed types, with no restriction on the choice of their contraction. The code implements the adjoint algorithmic differentiation that enables a very efficient evaluation of energy derivatives, comprising the ionic forces. Thus, one can perform structural optimizations and molecular dynamics in the canonical NVT ensemble at the VMC level. For the electronic part, a full WF optimization (Jastrow and antisymmetric parts together) is made possible, thanks to state-of-the-art stochastic algorithms for energy minimization. In the optimization procedure, the first guess can be obtained at the mean-field level by a built-in DFT driver. The code was efficiently parallelized by using a hybrid MPI-OpenMP protocol, which is also an ideal environment for exploiting the computational power of modern Graphics Processing Unit accelerators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Leveling of Polymer Grating Structures upon Heating: Dimension Dependence on the Nanoscale and the Effect of Antiplasticizers

The transition temperatures of nanoscale polymeric films are measured from a leveling experiment where a designed nanostructure is heated from below. Surface tension forces drive the relaxation of the polymeric features, allowing direct measurement of the critical temperature of collapse, T-flow and indirect measurement of the glass transition temperature, T-G. Small-angle X-ray scattering and atomic force microscopy are used to follow the leveling dynamics, whereas a mathematical model for the momentum balance is implemented to extract the viscosity of the polymer film as a function of temperature. Our methodology is illustrated in the context of films of poly(methyl methacrylate) that are patterned via nanoimprint lithography into dense gratings. We study how the glass transition temperature and the critical temperature of collapse vary as a function of the film size and the inclusion of the antiplasticizer, tris(2-chloropropyl) phosphate. The grating periods are varied consistently between 80 and 240 nm, whereas the antiplasticizer concentrations are 1, 3, 5, and 10 wt %. The solution of the momentum balance allows the detailed correlation between stresses, curvature, heating, and shear rates during leveling. We found that both temperatures, T-G and T-flow, decrease as the film size decreases or as the concentration of the antiplasticizer increases. In addition, antiplasticizer concentrations between 3 and 5 wt % stabilize the size dependence of T-flow. We show that the nature of the antiplasticizer is effectively to increase the low-temperature viscosity of the film. However, during leveling, the antiplasticized film sustains its curvature, thereby driving a sudden relaxation, once T-G is reached, and increasing the possibilities of defects.

glass transition↗

DOE OSTI2015

Theoretical study of IR and photoelectron spectra of small gallium-arsenide clusters

Relative stabilities of small Ga{sub n}As{sub m} clusters, as well as their structural electronic and vibrational properties, were computed and analysed using a CCSD(T) reference method since experimental data in this area are sparse or unknown. With the aim of investigating larger clusters, we explored several DFT functionals and basis sets able to mimic the reliable CCSD(T) approach. Among them, the PBE0/SBKJC+sp,d appears as the most efficient to describe the structural and vibrational properties since average differences of about 0.042Å and 5.1cm{sup −1} were obtained for bond lengths and fundamental vibrational frequencies, respectively for the first small clusters [1] of the series found from our GSAM method [2]. As further test, this model is used in order to investigate and revisit an experimental IR spectrum of Ga{sub n}As{sub m} mixture previously published by Li et al. [3]. More complicated is the difficulty which arises in the electronic description due to the presence of numerous low lying electronic states nearly degenerated to correctly describe the electronic structure. The case of Ga{sub 2}As will be discussed and the photoelectron spectra of the Ga{sub 2}As anion reanalyzed on the ground of our calculations [4] comparatively to the experimental spectra obtained by Neumark and co-workers [5].

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2019

Prediction of p K a s of Late Transition‐Metal Hydrides via a QM/QM Approach

Three implicit solvation models, the conductor‐like polarizable continuum model (C‐PCM), the conductor‐like screening model (COSMO), and universal implicit solvent model (SMD), combined with a hybrid two layer QM/QM approach (ONIOM), were utilized to calculate the p K a values, using a direct thermodynamic scheme, of a set of Group 10 transition metal (TM) hydrides in acetonitrile. To obtain the optimal combination of quantum methods for ONIOM calculations with implicit solvation models, the influence of factors, such as the choice of density functional and basis set, the atomic radii used to build a cavity in the solvent, and the size of the model system in an ONIOM scheme, was examined. Additionally, the impact of Grimme's empirical dispersion correction and exact exchange was also investigated. The results were calibrated by experimental data. This investigation provides insight about effective models for the prediction of thermodynamic properties of TM‐containing complexes with bulky ligands. © 2019 Wiley Periodicals, Inc.

Patel, Prajay↗

DOE OSTI2025

Kernel Manifolds: Nonlinear‐Augmentation Dimensionality Reduction Using Reproducing Kernel Hilbert Spaces

This paper generalizes recent advances on quadratic manifold (QM) dimensionality reduction by developing kernel methods-based nonlinear-augmentation dimensionality reduction. QMs, and more generally feature map-based nonlinear corrections, augment linear dimensionality reduction with a nonlinear correction term in the reconstruction map to overcome approximation accuracy limitations of purely linear approaches. While feature map-based approaches typically learn a least squares optimal polynomial correction term, we generalize this approach by learning an optimal nonlinear correction from a user-defined reproducing kernel Hilbert space. Our approach allows one to impose arbitrary nonlinear structure on the correction term, including polynomial structure, and includes feature map and radial basis function-based corrections as special cases. Furthermore, our method has relatively low training cost and has monotonically decreasing error as the latent space dimension increases. In conclusion, we compare our approach to proper orthogonal decomposition and several recent QM approaches on data from several example problems.

kernel methods↗