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DOE OSTI2023

Efficient QAOA Optimization using Directed Restarts and Graph Lookup

Variational Quantum Algorithms (VQA) aim to enhance the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. These algorithms utilize parameterized circuits and classical optimizers to iteratively execute circuits with varying parameters. However, VQA faces computational overheads due to repeated iterations and random restarts. Prior work suggests using basic sub-graphs to transfer parameters for the input graph, reducing optimizer overheads but limiting applicability to structured regular graphs. In real-world applications, random irregular graphs are common, and existing methods are not scalable or practical for such graphs. This paper presents a framework that aims to improve random irregular graphs in VQA. The framework uses graph similarity and important features like total edge counts, average edge counts, and variance. It follows an iterative process to choose basis sub-graphs from a small database and adjust parameters accordingly. Classical optimizers then utilize these parameters to determine when to restart and perform gradient descent. This approach increases the chances of reaching global maximum points.

Wang, Meng↗

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 OSTI2024

Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data

We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA (ReSA) reinitializes the stepsize sequence in each period, whereas Warm-start SA (WaSA) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions, ReSA and WaSA achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while WaSA accrues significantly lower computational cost as the number of periods increases. In addition, we present the regularized ReSA, which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.

Computer Science↗

DOE OSTI2025

Communication Lower Bounds and Optimal Algorithms for Symmetric Matrix Computations

In this article, we focus on the communication costs of three symmetric matrix computations: (i) multiplying a matrix with its transpose, known as a symmetric rank-k update (SYRK) (ii) adding the result of the multiplication of a matrix with the transpose of another matrix and the transpose of that result, known as a symmetric rank-2k update (SYR2K) (iii) performing matrix multiplication with a symmetric input matrix (SYMM). All three computations appear in the Level 3 Basic Linear Algebra Subroutines (BLAS) and have wide use in applications involving symmetric matrices. We establish communication lower bounds for these kernels using sequential and distributed-memory parallel computational models, and we show that our bounds are tight by presenting communication-optimal algorithms for each setting. Our lower bound proofs rely on applying a geometric inequality for symmetric computations and analytically solving constrained nonlinear optimization problems. As a result, the symmetric matrix and its corresponding computations are accessed and performed according to a triangular block partitioning scheme in the optimal algorithms.

Al Daas, Hussam [Rutherford Appleton Laboratory, D↗

DOE OSTI2020

Optimization of Lithium Content and Sintering Additives in Tape Cast Lithium Garnet Electrolyte Sheets

The lithium garnet (LLZO) remains one of the most promising electrolyte materials for producing solid state batteries. Tape casting is one method for producing the thin ceramic electrolyte layer required for a high-performance solid state battery. Processing of LLZO is complicated by several factors including lithium volatilization, abnormal grain growth and phase instability. Many of these issues are exacerbated in tape cast sheets by the sheets' higher surface area to volume ratio, when compared to pellet processing methods. Here, we report on an environmentally friendly aqueous tape casting process for LLZO using methylcellulose as a binder and compare final sheet properties to other, solvent-based, options. Solids loading, binder content, dispersant composition, and wetting agent type are varied to improve ease of casting and enhance properties of the as-cast tape. Lithium content in sintered LLZO sheets is controlled by the inclusion of excess Li 2 CO 3 as lithium source in the ceramic tape itself and by a novel method of producing a lithium-saturated furnace atmosphere. Sintering time and temperature are optimized, and rapid thermal processing is investigated as a method of reducing lithium loss. Al 2 O 3 and MgO are examined for use as sintering additives. Characterization of LLZO sheets is performed with XRD, EIS and SEM. Li 2 CO 3 and sintering additive levels are optimized for ionic conductivity and density. The optimization effort improved the conductivity and density of 100µm thick sintered sheets to greater than 3x10 -4 S/cm conductivity and 90% density.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Wrapper for the optimization of cross-section generation in OpenMC

The "Wrapper for the optimization of cross-section generation in OpenMC" is Python-based software that runs the open-source Monte Carlo code OpenMC (https://docs.openmc.org/en/stable/ ) to generate cross-sections for any reactor geometry. The wrapper then optimizes those cross sections. The optimization aims to choose an energy group structure and a scattering representation that maximize accuracy with respect to continuous-energy results while avoiding significant computational expense. It then outputs these cross-sections in an ISOXML format readable by the Idaho National Lab code suite MOOSE (Olin William Calvin, Mark D DeHart, “Architecture for the Performance of Nuclear Fuel Depletion Calculations”, Idaho National Laboratory report, November 2019). The expected use-cases of this software include: -finding the best group structure and scattering representation for a specific reactor -testing the appropriateness of energy group structures for different reactor types -comparing energy group structures and scattering representations to each other -generating cross sections for use in deterministic codes, including ones found in the MOOSE suite The example reactor geometry included in this release is a generic reactor design, not based on any reactor in existence or in development. It was fabricated for the sole purpose of being a “testbed-geometry” upon which to develop this tool. Since the tool is designed to be generic, the nature of the test geometry is not very important, however, it is valuable to include as an example for users who are unfamiliar with developing reactor geometries for OpenMC.

Kreher, Miriam↗

DOE OSTI2024

CMLM (Co-Optimized Machine-Learned Manifolds) [SWR-23-41]

Co-optimized Machine-Learned Manifolds (CMLM) is a data-driven approach for developing reduced-order manifold models for high-dimensional chemically reacting systems. It involves a specially designed neural network, the training of which simultaneously optimizes linear combinations of species that define the manifold, nonlinear mapping to outputs of interest such as reaction rates, and (optionally) subfilter closure for large eddy simulation. This software package provides an implementation of the CMLM approach in Python using the PyTorch machine learning library. A few example cases are included, showing how the tool can be applied to different types of data from 0D and 1D reacting simulations performed using Cantera. The neural networks can be saved in a format that is readable by the Pele suite of combustion solvers for use in reacting computational fluid dynamics simulations. This software repository contains several python scripts to perform various tasks associated with the Co-optimized Machine Learned Manifolds (CMLM) model, which is described in Perry, Henry de Frahan, and Yellapantula, CNF, 2022 (https://doi.org/10.1016/j.combustflame.2022.112286). This includes not only the code that defines the CMLM model, but also scripts to generate suitable training data, scripts to pre-process the data, scripts to train the CMLM model, and scripts to plot the output, as well as various other helper files. The scripts depend on several commonly used python libraries for data analysis and chemical reaction computations. The trained models that result from this tool are designed to work with the an interface being implemented in the Pele suite of reacting flow solvers (https://github.com/AMReX-Combustion).

Perry, Bruce↗

DOE OSTI2024

Parallel Variable Population Multi-Objective Optimizer (pvpmoo) v1.0

This is a parallel variable population multi-objective optimizer with an adaptive unified differential evolution algorithm or a genetic algorithm. It can also be used for single objective optimization. Some features of this code include: 1) The population size varies from generation to generation to save the total # of objective function evaluations. 2) The population is uniformly distributed to a number of parallel processors for simultaneous objective function evaluation. 3) The objective function evaluation can be attained from an external simulation program with control variables in its input file and objectives calculated from its output files. 4) The optimizer includes an adaptive unified differential evolution algorithm and a real value genetic algorithm. The parameters in the unified differential evolution algorithm can be chosen to attain any mutation schemes in the published literature.

Qiang, Ji↗

DOE OSTI2024

Interpolation-Based Composite Derivative-Free Optimization (IBCDFO)

IBCDFO is a library of interpolation-based optimization methods for composite derivative-free optimization. These methods are applicable when optimizing a function that depends on multiple outputs from a blackbox experiment/simulation for which derivatives with respect to the decision variables are not available. The methods include POUNDerS and GOOMBAH.

Wild, Stefan [Lawrence Berkeley National Laborator↗

DOE OSTI2025

Optimal Mitigation Planning For Adversarial Scenarios

We propose a generalized framework which performs an optimal partitioning of a limited budget into various organizational sectors in order to improve the cybersecurity of a smart device or component in the Cyber Physical Energy System (CPS). The framework identifies the adversarial threats and possible attack sequences which can be performed to exploit cyber vulnerabilities of the component. Thereafter, we formulate an Mixed Integer Linear Programming (MILP) optimization problem which aims to evaluate the optimal budget partitions in order to minimize the number of highly likely attack sequences. Though we provide results for using the framework in CPES, the proposed methodology can be extended for multiple domains with a set of known adversarial and mitigation actions.

Purohit, Sumit [Pacific Northwest National Laborat↗

DOE OSTI2025

Q-OPT:Quantum Optimization Toolkit

SF-2025-003 Quantum computing has the potential to solve classical optimization problems. To bring these algorithms into practical use, a comprehensive, high-performance and user-friendly toolkit is essential. The Q-OPT: Quantum Optimization Toolkit is a collection of software tools designed to support complete end-to-end framework for quantum optimization.

Hovland, Paul [Argonne National Laboratory (ANL), ↗

DOE OSTI2025

Plant Reload Optimization (prlo)

The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.

Kim, Junyung [Idaho National Laboratory] (00090005↗

DOE OSTI2026

Adaptive Sampling Trust Region Method for Bi-fidelity Simulation Optimization [SWR-25-166]

Adaptive Sampling Trust Region Method for Bi-fidelity Simulation Optimization aims to demonstrate the effect of adaptive sampling-based bi-fidelity stochastic trust region method (ASTRO-BFDF). ASTRO-BFDF, derived from a derivative-free adaptive sampling trust-region optimization (ASTRO-DF) (Shashaani et al. 2018, Ha and Shashaani 2023), intended to efficiently solve the bi-fidelity simulation optimization.

Mueller, Juliane [National Laboratory of the Rocki↗

DOE OSTI2021

Structural design and optimization of a series of 13.2 MW downwind rotors

The quest for reduced LCOE has driven significant growth in wind turbine size. A key question to enable larger rotor designs is how to configure and optimize structural designs to constrain blade mass and cost while satisfying a growing set of challenging structural design requirements. In this paper, we investigate the performance of a series of three two-bladed downwind rotors with different blade lengths (104.3-m, 122.9-m, and 143.4-m) all rated at 13.2 MW. The primary goals are to achieve 25% rotor mass and 25% LCOE reduction. A comparative analysis of the structural performance and economics of this family rotors is presented. To further explore optimization opportunities for large rotors, we present new results in a root and spar cap design optimization. In summary, we present structural design solutions that achieve 25% rotor mass reduction in a SUMR13i design (104.3-m) and 25% LCOE reduction in a SUMR13C design (143.4-m).

Yao, Shulong↗

DOE OSTI2021

Aero-structural design and optimization of 50 MW wind turbine with over 250-m blades

The quest for reduced levelized cost of energy has driven significant growth in wind turbine size; however, larger rotors face significant technical and logistical challenges. The largest published rotor design is 25 MW, and here we consider an even larger 50 MW design with blade length over 250 m. This paper shows that a 50 MW design is indeed possible from a detailed engineering perspective and presents a series of aero-structural blade designs, and critical assessment of technology pathways and challenges for extreme-scale rotors. The 50 MW rotor design begins with Monte Carlo simulations focused on optimizing carbon spar cap and root design. A baseline design resulted in a 250-m blade with mass of 502 tonnes. Subsequently, an aero-structural design and optimization were performed to reduce the blade mass/cost with more than 25% mass reduction and 30% cost reduction by determining optimal blade chord and airfoil thickness for best aero-structural performance.

Yao, Shulong↗

DOE OSTI2021

Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization

In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computational cost associated with engine design optimization. However, such methods still suffer from drawbacks. One main disadvantage is that the default machine learning (ML) hyperparameters are often severely suboptimal for a given problem. This has often been addressed by manually trying out different hyperparameter settings, but this solution is ineffective in case of a high-dimensional hyperparameter space. Besides this problem, the amount of data needed for training is also not known a priori. In response to these issues that need to be addressed, the present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. In this approach, a Bayesian optimization technique is used to find the best machine learning hyperparameters based on an initial dataset obtained from a small number of CFD simulations. Subsequently, a genetic algorithm is employed to locate the design optimum on the ML surrogate surface. In the vicinity of the design optimum, the solution is refined by repeatedly running CFD simulations at the projected optima and adding the newly obtained data to the training dataset. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations, compared to the use of default hyperparameters. The proposed framework offers the advantage of being a more hands-off approach that can be readily utilized by researchers and engineers in industry who do not have extensive machine learning expertise.

Owoyele, Opeoluwa↗

DOE OSTI2025

Optimal production of Phanerochaete chrysosporium manganese peroxidases and Trametes sp. C30 laccase hybrid Lac131 in Aspergillus niger for lignin bioconversion

Background Incorporating the production of related ligninolytic enzymes into industrial filamentous fungus Aspergillus niger will enhance the bioconversion of lignocelluloses to various chemical products. Results In this study, transgenic expression of Phanerochaete chrysosporium manganese peroxidases (mnps) and Trametes sp. C30 laccase hybrid Lac131 (lac131) were examined and optimized in A. niger 11414 prtT∆ strain. Five mnps (mnp1, mnp2, mnp3, mnp4, and mnp5) and lac131 genes were expressed separately or in combination. The transgenic strain containing the entire mnp2 genomic coding sequence (gmnp2) exhibited the highest mnP activity among the five mnp over-expression strains in the modified minimal medium (mMM) with addition of 5 g/L bovine hemoglobin (bHg). We examined the effects of hemin and bHg on mnP production in the gmnp2 strain cultures and found that at least 1 g/L bHg was required, while hemin was not. Culture conditions for mnP production were further optimized for the gmnp2 strain and the highest mnP activities were detected in the cultures grown at 25 °C and 200 rpm with an initial pH of 4.5. Effects of soy protein, skim milk, and bovine serum albumin on mnP production were investigated; 5 g/L of soy proteins or skim milk had comparable effects to 2.5 g/L bHg, while cultures with bovine serum albumin had diminished mnP activity. Disruption of both prtT and vsm1 substantially augmented the mnP production and its activity reached 575 U/L. Trametes sp. C30 laccase hybrid lac131 was strongly expressed in either A. niger gmnp2 (1975 U/L) or 11414prtT∆ (3895 U/L) strain. Both mnP and laccase in the culture supernatants effectively decolorized selected phenolic compounds (dyes) and cleaved tagged model lignin dimers. Conclusion The mnP was successfully produced in A. niger by optimizing the culture conditions and host strain. Co-expression of all four mnp genes in the same expression host by multiplex CRISPR will lead to the mnP production reaching levels comparable to P. chrysosporium, while only requiring 36 h at 25 °C. The Lac131 activity in transgenic A. niger strain is 4- to 7-times higher than that in previous studies. Co-production of mnP and laccase in A. niger will enhance the lignin bioconversion efficiency.

Aspergillus niger↗

DOE OSTI2020

Optimization-Driven Scenario Grouping

Scenario decomposition algorithms for stochastic programs compute bounds by dualizing all nonanticipativity constraints and solving individual scenario problems independently. Here, we develop an approach that improves on these bounds by reinforcing a carefully chosen subset of nonanticipativity constraints, effectively placing scenarios into groups. Specifically, we formulate an optimization problem for grouping scenarios that aims to improve the bound by optimizing a proxy metric based on information obtained from evaluating a subset of candidate feasible solutions. We show that the proposed grouping problem is NP-hard in general, identify a polynomially solvable case, and present two formulations for solving the problem: a matching formulation for a special case and a mixed-integer programming formulation for the general case. We use the proposed grouping scheme as a preprocessing step for a particular scenario decomposition algorithm and demonstrate its effectiveness in solving standard test instances of two-stage 0–1 stochastic programs. Using this approach, we are able to prove optimality for all previously unsolved instances of a standard test set. Additionally, we implement this scheme as a preprocessing step for PySP, a publicly available and widely used implementation of progressive hedging, and compare this grouping approach with standard grouping approaches on large-scale stochastic unit commitment instances. Finally, the idea is extended to propose a finitely convergent algorithm for two-stage stochastic programs with a finite feasible region.

97 MATHEMATICS AND COMPUTING↗