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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II

Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000$\times$. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership Computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function.

Coletti, Mark↗
DOE OSTI2021

Arrays of Si vacancies in 4 H -SiC produced by focused Li ion beam implantation

Point defects in SiC are an attractive platform for quantum information and sensing applications because they provide relatively long spin coherence times, optical spin initialization, and spin-dependent fluorescence readout in a fabrication-friendly semiconductor. The ability to precisely place these defects at the optimal location in a host material with nano-scale accuracy is desirable for integration of these quantum systems with traditional electronic and photonic structures. Here, we demonstrate the precise spatial patterning of arrays of silicon vacancy (${V}_{Si}$) emitters in an epitaxial 4H -SiC (0001) layer through mask-less focused ion beam implantation of Li + . We characterize these arrays with high-resolution scanning confocal fluorescence microscopy on the Si-face, observing sharp emission lines primarily coming from the ${V1}^{{\prime}}$ zero-phonon line (ZPL). The implantation dose is varied over 3 orders of magnitude, leading to ${V}_{Si}$ densities from a few per implantation spot to thousands per spot, with a linear dependence between ZPL emission and implantation dose. Optically-detected magnetic resonance (ODMR) is also performed, confirming the presence of V2 ${V}_{Si}$. Our investigation reveals scalable and reproducible defect generation.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Parallel implementation and performance optimization of the configuration-interaction method

The configuration-interaction (CI) method, long a popular approach to describe quantum many-body systems, is cast as a very large sparse matrix eigenpair problem with matrices whose dimension can exceed one billion. Such formulations place high demands on memory capacity and memory bandwidth - - two quantities at a premium today. In this paper, we describe an efficient, scalable implementation, BIGSTICK, which, by factorizing both the basis and the interaction into two levels, can reconstruct the nonzero matrix elements on the fly, reduce the memory requirements by one or two orders of magnitude, and enable researchers to trade reduced resources for increased computational time. We optimize BIGSTICK on two leading HPC platforms - - the Cray XC30 and the IBM Blue Gene/Q. Specifically, we not only develop an empirically-driven load balancing strategy that can evenly distribute the matrix-vector multiplication across 256K threads, we also developed techniques that improve the performance of the Lanczos reorthogonalization. Combined, these optimizations improved performance by 1.3-8× depending on platform and configuration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Enhanced red luminescence quantum yield from Gd3+/Eu3+: CaLa2ZnO5 phosphor spheres for photonic applications

Highlights: • Ravishing red emission is observed from Gd{sup 3+}/Eu{sup 3+}: CaLa{sub 2}ZnO{sub 5} phosphor spheres. • Red emission of Eu{sup 3+} was significantly enhanced by co-doping with Gd{sup 3+}. • Effective sensitization of Gd{sup 3+} ions and enhanced quantum yield have been elucidated. • Energy transfer from Gd{sup 3+} to Eu{sup 3+} was demonstrated by several fluorescence dynamics. • CIE coordinates are very close to National Television Standard Committee (NTSC). - Abstract: Novel bright red emission was obtained from co-doped Gd{sup 3+}/Eu{sup 3+}: CaLa{sub 2}ZnO{sub 5} (CLZO) phosphor spheres. We have successfully synthesized co-doped Gd{sup 3+}/Eu{sup 3+}: CLZO phosphor spheres by citrate sol-gel method. Structural and morphological analysis was carried out by XRD and FE-SEM. EDS and XPS studies were employed to analyze the presence of elements and their ionic states respectively. The complex formation studies were carried out by the FTIR spectral study. The photoluminescence spectral profiles confirm the dazzling red emission has been observed at 626 nm ({sup 5}D{sub 0} → {sup 7}F{sub 2}) from the Eu{sup 3+}: CLZO phosphors under the excitation of 467 nm. Upon increasing the Eu{sup 3+} concentration, the photoluminescence performance has also increased remarkably. The optimized concentration of the Eu{sup 3+} was noticed at 12 mol%. Upon co-doping with Gd{sup 3+} ions to Eu{sup 3+}: CLZO phosphors, the red emission pertaining to Eu{sup 3+} was appreciably enhanced through energy transfer from Gd{sup 3+} to Eu{sup 3+} ions. The energy transfer mechanism was substantiated by several fluorescent methods such as lifetime decay dynamics and quantum efficiencies. Commission International de I’Echairage (CIE) 1931 chromaticity coordinates (x–y) calculated for singly doped and co-doped CLZO samples. The CIE coordinates were found to be (0.6644, 0.3355) and (0.6688, 0.3311) respectively, which were good consistent with the standard values of NTSC surprisingly. Correlated color temperature (CCT) value was evaluated for optimised co-doped sample and it was found to be between 2000–5000 K which could be more preferable red component for warm white light applications. The lifetime and quantum efficiency of the optimized co-doped sample have been evaluated as 1.102 ms and 69% respectively. The ravishing red emission of the Gd{sup 3+}/Eu{sup 3+}: CLZO phosphor spheres which suggests that these phosphor materials could be promising candidates for red luminescent optical devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Multiobjective optimization of nuclear microreactor reactivity control system operation with swarm and evolutionary algorithms

To improve the marketability of novel microreactor designs, there is a need for automated and optimal control of these reactors. This paper presents a methodology for performing multiobjective optimization of control drum operation for a microreactor under normal and off-nominal conditions. Here, two different case studies are used where the control drum configuration is optimized for the reactor to be critical with some desired power distribution that would satisfy peaking limits. A surrogate model for power distribution is developed based on a feedforward neural network. The process for determining weights for scalarization of the multiobjective optimization problem is also detailed. Six optimization algorithms: evolutionary strategies, differential evolution, grey wolf optimization, Harris hawks optimization, moth flame optimization and particle swarm optimization, are all applied to these cases and the results analyzed. Although all these algorithms will demonstrate optima-seeking behavior, for real-time control it is necessary to identify the best algorithm to efficiently provide reasonable optima without operator interference. The moth flame optimization algorithm was found to perform particularly well on both cases. Overall, it was found that the algorithms capable of supplying the best optima were also the most consistent. Finally, the found optima were verified with the original model used to train surrogates.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Omnidirectional Harvesting of Weak Light Using a Graphene Quantum Dot-Modified Organic/Silicon Hybrid Device

Despite great improvements in traditional inorganic photodetectors and photovoltaics, more progress is needed in the detection/collection of light at low-level conditions. Traditional photodetectors tend to suffer from high noise when operated at room temperature; therefore, these devices require additional cooling systems to detect weak or dim light. Conventional solar cells also face the challenge of poor light-harvesting capabilities in hazy or cloudy weather. The real world features such varying levels of light, which makes it important to develop strategies that allow optical devices to function when conditions are less than optimal. In this work, we report an organic/inorganic hybrid device that consists of graphene quantum dot-modified poly(3,4-ethylenedioxythiophene) polystyrenesulfonate spin-coated on Si for the detection/harvest of weak light. The hybrid configuration provides the device with high responsivity and detectability, omnidirectional light trapping, and fast operation speed. To demonstrate the potential of this hybrid device in real world applications, we measured near-infrared light scattered through human tissue to demonstrate noninvasive oximetric photodetection as well as characterized the device's photovoltaic properties in outdoor (i.e., weather-dependent) and indoor weak light conditions. In conclusion, this organic/inorganic device configuration demonstrates a promising strategy for developing future high-performance low-light compatible photodetectors and photovoltaics.

14 SOLAR ENERGY↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Correlation functions from a unified variational principle: Trial Lie groups

Time-dependent expectation values and correlation functions for many-body quantum systems are evaluated by means of a unified variational principle. It optimizes a generating functional depending on sources associated with the observables of interest. It is built by imposing through Lagrange multipliers constraints that account for the initial state (at equilibrium or off equilibrium) and for the backward Heisenberg evolution of the observables. The trial objects are respectively akin to a density operator and to an operator involving the observables of interest and the sources. We work out here the case where trial spaces constitute Lie groups. This choice reduces the original degrees of freedom to those of the underlying Lie algebra, consisting of simple observables; the resulting objects are labeled by the indices of a basis of this algebra. Explicit results are obtained by expanding in powers of the sources. Zeroth and first orders provide thermodynamic quantities and expectation values in the form of mean-field approximations, with dynamical equations having a classical Lie–Poisson structure. At second order, the variational expression for two-time correlation functions separates–as does its exact counterpart–the approximate dynamics of the observables from the approximate correlations in the initial state. Two building blocks are involved: (i) a commutation matrix which stems from the structure constants of the Lie algebra; and (ii) the second-derivative matrix of a free-energy function. The diagonalization of both matrices, required for practical calculations, is worked out, in a way analogous to the standard RPA. The ensuing structure of the variational formulae is the same as for a system of non-interacting bosons (or of harmonic oscillators) plus, at non-zero temperature, classical Gaussian variables. This property is explained by mapping the original Lie algebra onto a simpler Lie algebra. The results, valid for any trial Lie group, fulfill consistency properties and encompass several special cases: linear responses, static and time-dependent fluctuations, zero- and high-temperature limits, static and dynamic stability of small deviations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Machine-learning accelerated geometry optimization in molecular simulation

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. Here, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Extended Lagrangian Born–Oppenheimer molecular dynamics using a Krylov subspace approximation

It is shown how the electronic equations of motion in extended Lagrangian Born–Oppenheimer molecular dynamics simulations can be integrated using low-rank approximations of the inverse Jacobian kernel. This kernel determines the metric tensor in the harmonic oscillator extension of the Lagrangian that drives the evolution of the electronic degrees of freedom. The proposed kernel approximation is derived from a pseudoinverse of a low-rank estimate of the Jacobian, which is expressed in terms of a generalized set of directional derivatives with directions that are given from a Krylov subspace approximation. The approach allows a tunable and adaptive approximation that can take advantage of efficient preconditioning techniques. The proposed kernel approximation for the integration of the electronic equations of motion makes it possible to apply extended Lagrangian first-principles molecular dynamics simulations to a broader range of problems, including reactive chemical systems with numerically sensitive and unsteady charge solutions. This can be achieved without requiring exact full calculations of the inverse Jacobian kernel in each time step or relying on iterative non-linear self-consistent field optimization of the electronic ground state prior to the force evaluations as in regular direct Born–Oppenheimer molecular dynamics. We note the low-rank approximation of the Jacobian is directly related to Broyden’s class of quasi-Newton algorithms and Jacobian-free Newton–Krylov methods and provides a complementary formulation for the solution of nonlinear systems of equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Designing and optimizing highly efficient grating for high-brightness laser based on spectral beam combining

A highly efficient nano-periodical grating is theoretically investigated for spectral beam combining (SBC) and is experimentally implemented for attaining high-brightness laser from a diode laser array. The rigorous coupled-wave analysis with the S matrix method is employed to optimize the parameters of the grating. According the optimized parameters, the grating is fabricated and plays a key role in SBC cavity. The diffraction efficiency of this grating is optimized to 95% for the output laser which is emitted from the diode laser array. The beam parameter product of 3.8 mm mrad of the diode laser array after SBC is achieved at the output power of 46.3 W. The optical-to-optical efficiency of SBC cavity is measured to be 93.5% at the maximum operating current in the experiment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

The Effect of Base Thickness on Photoconversion Efficiency in Textured Silicon-Based Solar Cells

A theory is developed, which describes the experimental external quantum efficiency, EQE(λ) as a function of photon wavelength for structured Si-based solar cells. Short-circuit current density as a function of base thickness, d, is calculated for the high-efficiency solar cells with the photoconversion efficiency η ≥ 25% The procedure allows one to carry out a complete optimization of such solar cells, in particular, to find the optimal base thickness, d{sub opt}.

14 SOLAR ENERGY↗
DOE OSTI2026

Ground and excited state gradients with end-to-end differentiable semiempirical quantum chemistry

Accurate and efficient gradients of molecular energy with respect to nuclear degrees of freedom are essential for geometry optimization and molecular dynamics, including simulations that go beyond the Born–Oppenheimer regime. A common approach involves deriving analytical formulas for new electronic structure methods, which is often conceptually difficult and requires tedious coding. Here, we implement analytical, semi-numerical, and automatic differentiation (AD)-based gradient pathways for semiempirical Hamiltonian models in the PYSEQM software package, leveraging both graphics processing unit (GPU) and central processing unit (CPU) architectures. We further extend these capabilities to excited states calculated using the configuration interaction singles and time-dependent Hartree–Fock ansätze. We benchmark wall time, peak memory usage, and accuracy across three molecular families of varying chemical complexity, including systems of up to a thousand atoms. For ground-state simulations, analytical and AD gradients achieve near-identical GPU runtimes, while semi-numerical gradients are slower on GPU but remain competitive on CPU. For excited states, both analytical and custom AD approaches using implicit differentiation show similar performance and low memory requirements, whereas gradients with full AD are memory-limited. AD gradients match analytical ones in accuracy across all tested systems, aided by a quaternion-based diatomic frame rotation for two-center quantities that ensures smooth energy surfaces. Overall, automatic differentiation emerges as a practical alternative to analytical gradients in semiempirical quantum chemistry, offering high accuracy while allowing seamless integration in AI-driven workflows and popular packages, such as PyTorch and JAX. Our results provide actionable guidance for selecting optimal gradient strategies in large-scale ground- and excited-state molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Preliminary study of auto-differentiation algorithm in beam dynamics with stochastic process

Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.

Accelerator Physics↗
DOE OSTI2017

Highly stable cesium lead iodide perovskite quantum dot light-emitting diodes

Recently, all-inorganic perovskites such as CsPbBr3 and CsPbI3, have emerged as promising materials for light-emitting applications. While encouraging performance has been demonstrated, the stability issue of the red-emitting CsPbI3 is still a major concern due to its small tolerance factor. Here we report a highly stable CsPbI3 quantum dot LED with red emission fabricated using an improved purification approach. The device achieved decent external quantum efficiency (EQE) of 0.21 % at a bias of 6 V and outstanding operational stability, with a L70 lifetime (EL intensity decreases to 70% of starting value) of 16 h and 1.5 h under a constant driving voltage of 5 V and 6 V (maximum EQE operation) respectively. Furthermore, the device can work under a higher voltage of 7 V (maximum luminance operation) and retain 50% of its initial EL intensity after 500 s. These findings demonstrate the promise of CsPbI3 quantum dots for stable red LEDs, and suggest the feasibility for electrically pumped perovskite lasers with further device optimizations.

14 SOLAR ENERGY↗
DOE OSTI2015

Optimization of the antireflection coating of thin epitaxial crystalline silicon solar cells

In this study we use an effective weighting function to include the internal quantum efficiency (IQE) and the effective thickness, Te, of the active cell layer in the optical modeling of the antireflection coating (ARC) of very thin crystalline silicon solar cells. The spectrum transmitted through the ARC is hence optimized for efficient use in the given cell structure and the solar cell performance can be improved. For a 2-μm thick crystalline silicon heterojunction solar cell the optimal thickness of the Indium Tin Oxide (ITO) ARC is reduced by ~8 nm when IQE data and effective thickness are taken into account compared to the standard ARC optimization, using the AM1.5 spectrum only. The reduced ARC thickness will shift the reflectance minima towards shorter wavelengths and hence better match the absorption of very thin cells, where the short wavelength range of the spectrum is relatively more important than the long, weakly absorbed wavelengths. For this cell, we find that the optimal thickness of the ITO starts at 63 nm for very thin (1 μm) active Si layer and then increase with increasing T e until it saturates at 71 nm for T e > 30 μm.

14 SOLAR ENERGY↗
DOE OSTI2018

Optimal Design and Control of Qubits

Research interest in developing computing systems that represent logic states using quantum mechanical observables has only increased in the few decades since its inception. While quantum computers, with Josephson junction based qubits, have now been commercially available in the last three years, there is also significant research initiative to develop scalable quantum computers with so-called donor qubits. B.E. Kane first published on a device implementation of a silicon-based quantum computer in 1998, which sparked a wave of follow-on advances due to the attractive nature of silicon-based computing[7]. Nearly all commercial computing systems using classical binary logic are fabricated using a silicon substrate and it is inarguably the most mature material system for semiconductor devices, so that coupling classical and quantum bits on a single substrate is possible. The process of growing and processing silicon crystals into wafers is extremely robust and leads to minimal impurities or structural defects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Comparative S/TEM study of superconducting Ta quantum resonators by wet and dry etching types

Superconducting resonators play a pivotal role in various quantum technology applications, such as quantum computing and high-frequency communication systems. The performance of these resonators is closely tied to the properties of the superconducting films used in their fabrication. Here, in this study, we investigated the impact of wet and dry etching on tantalum (Ta) films leveraging advanced scanning transmission electron microscopy-based characterization methods and examined the morphological, chemical, and strain changes caused by the etching processes. Consequently, we report the significant differences between the two etching methods, with dry etching resulting in straight slanted sidewalls and a thinner oxidized layer, while wet etching produced curved sidewalls and undercuts. Both methods led to the formation of a residual Ta wedge at the lower part of the sidewall, causing lattice deformation, which could adversely influence the homogeneous operations of superconducting devices. These insights enhance our understanding of how etching influences superconducting films, offering valuable guidance for optimizing resonators and related devices. Our findings mark a significant stride in advancing quantum technologies and high-frequency communication by enhancing our practical understanding of superconducting material fabrication.

36 MATERIALS SCIENCE↗