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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Efficient Reformulation and Optimization for SC-ACOPF with Line Switching

This project aims to develop efficient and robust computational methods for solving the security-constrained alternating current optimal power flow problem (SC-ACOPF). The SC-ACOPF problem is a central problem in operating the electric power grids in the United States. It determines the most economically efficient way to operate the generation and transmission system to meet daily electricity demand. The solution found by solving an SC-ACOPF problem must satisfy the physics of the alternating current (AC) power flows, various generator and network operational constraints, and must maintain secure operation under various contingency scenarios, where a generator, a transmission branch, or a transformer may unexpectedly trip offline.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
NASA NTRSDate not supplied

Structured Covariance Gaussian Networks for Orion Crew Module Aerodynamic Uncertainty Quantification

In this paper we propose a new approach for nonlinear regression and uncertainty quantification. The method is based on a pair of neural networks which parameterize mean and dense covariance functions of a multivariate Gaussian process, trained together to maximize the log-likelihood of observing the given data. The covariance matrix is made positive definite at every input by construction. We also propose a sampling approach that produces viable surrogate function realizations from the Gaussian process. We call the proposed model a Structured Covariance Gaussian Network (SCGN). We illustrate the use of SCGNs for learning an aerodynamic response surface with built-in uncertainty for the Orion crew module. We find that SCGN provides an efficient and systematic way to learn nonlinear functional relationships and dense covariances. We compare results to a baseline Gaussian process regressor and observe that the SCGN provides comparable uncertainty descriptions with improved scalability to dataset size. The sample functions generated by SCGN are fast to evaluate online and are therefore convenient for use in trajectory simulations. These results suggest that SCGN may be a viable computational method for aerodynamic uncertainty quantification.

machine learning↗
DOE OSTI2019

Rapid Multiscale Computational Screening for OLED Host Materials

The design of new host materials for phosphorescent OLEDs is challenging because several physical property requirements must be met simultaneously. A triplet energy (E T ) higher than that of the chosen emitting dopant, appropriate HOMO/LUMO energy levels, good charge carrier transport, and high stability are all required. Here, computational methods were used to screen structures to find the most promising candidates for OLED hosts. The screening was carried out in three Tiers. The Tier 1 selection, based on Density Functional Theory calculations identified a set of eight molecular structures with E T > 2.9 eV, suitable for hosting blue phosphorescent dopants such as iridium(III)bis((4,6-di-fluorophenyl)-pyridinato-N,C2’)picolinate (FIrpic). Phenanthro[9,10 d]imidazole was chosen as the starting point for the Tier 2 selection. Thirty seven unique molecular structures were enumerated by isoelectronic nitrogen transmutation of up to two CH fragments of the phenanthrene. Three molecules, i.e. imidazo[4,5 f] phenanthrolines with nitrogens at the 1,10 , 3,8 and 4,7 positions, were selected for Tier 3, which involved the use of molecular dynamics simulations and electron coupling calculations to predict differences in charge transport between the three materials. The three were explored experimentally through synthesis and device fabrication. The singlet, triplet, and frontier orbital energies computed using single molecule DFT calculations (tiers 1 and 2) were consistent with the experimental values in fluid solution, and the multi scale modeling scheme (tier 3) correctly predicted the poor device performance of one material. Here, we conclude that screening host materials using only single molecule quantum mechanical data was not sufficient to predict whether a given material would make a good OLED host with certainty, however they can be used to screen out materials that are destined to fail due to low singlet/triplet energies or a poor match of the frontier orbital energies to the dopant or transport materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Optimization of Conditions for Photoluminescence-Based Sensing of Critical Minerals: Strategies and Outlook

Economically critical minerals and metals are vital to America’s security, with application areas in energy, advanced technologies, and defense systems. The growing implementation of renewable energy sources such as electric vehicles is expected to significantly increase demand for these minerals, while the global supply is monopolistic, with most of the production occurring in a few countries outside of the US. To mitigate potential supply chain vulnerabilities, the domestic production of economically critical metals has become a bipartisan priority of the US government, and unconventional sources such as coal and its utilization byproducts such as fly ash and acid mine drainage are being actively explored as an alternative method for meeting domestic demand. Slow and expensive characterization methods for critical metals present a significant pain point for domestic production, which has led to the exploration of alternative low-cost, portable platforms capable of detecting target metals during resource prospecting and downstream process monitoring. The National Energy Technology Laboratory has developed a portable fiber optic-based luminescence spectrometer that utilizes a metal-organic framework (MOF) material to detect and distinguish parts-per-billion levels of the visible-emitting rare earth elements terbium, dysprosium, samarium, and europium, which are all economically critical metals. Although preliminary results with this system are promising, a crucial barrier to practical deployment is the ability to operate in low pH, high ionic strength environments, as acidic conditions and the presence of other metal ions can significantly reduce luminescence signal. Here, current strategies related to sample treatment, material design, and sensor configuration are discussed in the context of improving sensor performance in application-relevant conditions. Additionally, future opportunities, such as the optimization of the sensing binding environment through computational methods and specific metals to be removed for treatment will be discussed, presenting a forward-looking view for innovation in luminescent sensing of critical metals.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Validation of Oregon State University High Temperature Test Facility Experiments Using Pronghorn

The OSU High Temperature Test Facility is a quarter-scale diameter, 1/64 scale volume test facility meant to replicate thermophysical phenomena in the prototypical General Atomics Modular High Temperature Gas Reactor. Tests pertaining to conduction cooldown events were performed from 2016-2019, providing a large database by which computational methods that are applicable to different length scales can be validated. One of these codes is Pronghorn, which is a coarse-meshed, porous-based subchannel thermal hydraulics code based on the MOOSE application, with the intention of better capturing the physics of both conduction and convection heat transfer within the OSU HTTF core. The goal of this summer project is to develop the framework by which Pronghorn can perform validation exercises of the HTTF core for benchmarking, by generating a mesh appropriate to the geometry of the HTTF core, developing input decks that accurately capture the initial and boundary conditions, materials, and relevant equations to the physics seen in the HTTF core, and using a postprocessor to compare simulation results to various experimental data. While validation of codes is a multi-year project, a mesh has been generated and tested in Pronghorn that meets mass conservation and basic heat transfer principles. The next step is to accurate depict the fluid inlet and outlet boundary conditions, which will be performed using computational fluid dynamics software.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

High temperature stability and transport characteristics of hydrogen in alumina via multiscale computation

Here, the impact of hydrogen charge states on the stability and transport characteristics of hydrogen interstitials in alumina polymorphs is evaluated by multiscale computational methods including density functional theory (DFT), ab initio molecular dynamics (AIMD) and machine learned force fields. Thermodynamic calculations show that the protonic H i +1 interstitial is the most stable defect species for most values of the electronic bandgap in both and amorphous alumina (Al 2 O 3 ). Further, active learned Gaussian approximation potentials (GAP) were developed using AIMD data to study temperature dependent long time proton diffusion in alumina. Diffusivity calculations from GAP-MD simulations are found to be comparable with of the AIMD data, while being ~340 times faster and scalable to larger systems. Comparisons with diffusivity values for other interstitial charge states (H i 0 and H i -1 ) and published experimental literature indicate that H i +1 diffusion is the likely mechanism of hydrogen transport. A good agreement is obtained between H i +1 diffusivity calculated in α-Al 2 O 3 from DFT: 5.05 10 -3 exp(-0.81 eV/k B /T) cm 2 /s and reported experiment: 9.7X10 -4 exp(-0.83 eV/k B /T) cm 2 /s. Computationally and experimentally calculated energy barriers (0.81 and 0.83 eV respectively) only differ by 2.5%. Similarly, the pre-exponential diffusion coefficients only differ by 0.5 orders of magnitude. Moreover, the diffusivity of H i +1 in amorphous Al 2 O 3 in the 1000–2000 K range is calculated to be 2.53X10 -2 exp(-0.89 eV/k B /T), just one order of magnitude higher than the corresponding value in α-Al 2 O 3 . This suggests that local structural disorder does not significantly affect the energy landscape and diffusion behavior of H i +1 in Al 2 O 3 . Overall, these results show promise for the application of alumina polymorphs as hydrogen permeation barriers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRSDate not supplied

FUN3D Analyses in Support of the 1st AIAA Stability and Control Prediction Workshop

The 1st AIAA Stability and Control Prediction Workshop was created to establish best practices for the prediction of stability & control derivatives using computational fluid dynamics and assess the limitations of these computational methods when those best practices are applied. The inaugural workshop considers the ONERA version of the NASA/Boeing Common Research Model(CRM), which includes the wing, body, horizontal tail, and a vertical tail designed by ONERA. Wind tunnel tests have been conducted for this configuration with longitudinal tests having been previously published, in addition to unpublished data at small sideslip angles that will serve as ‘blind’ data for workshop data comparisons. Participants were provided a ‘family’ of unstructured grids for the full-span ONERA CRM model with the wind-tunnel sting included. This family of mixed-element grids consists of 5 levels of refinement (tiny, coarse, medium, fine, and extra fine) with surface and volume mesh scaling, resulting in a size range of 14.6 to 53.4 million nodes. In addition, a medium refinement mesh has been provided for the ONERA CRM configuration without a sting to evaluate the sting’s impact on static longitudinal stability characteristics. In addition to these workshop-provided grids, the present work also considers an equivalent ‘family’ of computational grids generated using Heldenmesh™, a rapid grid generation software by Helden Aerospace Corporation for creating high-quality, three-dimensional, mixed-element unstructured meshes. Because of the authors’ familiarity with this software, these additional grids were generated as a comparison to the workshop-provided grids and to better understand the implications of using volume-mirrored grids for stability and control predictions. The present work will contribute to the workshop with test case data generated using the NASA FUN3D code, a parallelized, unstructured, node-based, finite-volume discretization, Reynolds-averaged Navier-Stokes flow solver. Numerical simulations will be conducted using the Quadratic Constitutive Relationship (QCR) version of the Spalart-Allmaras (SA) turbulence model with negative turbulence variable provisions. Both steady and2nd-order, time-accurate simulation results are to be generated and compared for select test cases, as time permits, to investigate their impact on FUN3D predictions. The present work will consider the three primary workshop test cases: (1) grid convergence study, (2) Mach number effect on static stability, and (3) wind tunnel sting increments. Additionally, data will be provided for the two optional test cases, which include:(1) static stability derivative calculations and (2)sideslip angle sweeps. In each of the test cases, the vehicle is stationary, and the body is assumed to be rigid, where vehicle deformation has been accounted for in the model configuration geometry. For all test cases, longitudinal and lateral force and moment aerodynamic coefficients will be provided for the total configuration, in addition to a component-level breakdown that includes the port wing, starboard wing, fuselage, and tail.

CFD↗
DOE OSTI2020

Advancement of Actinide Metal–Organic Framework Chemistry via Synthesis of Pu-UiO-66

We report the synthesis and characterization of the first plutonium metal–organic framework (MOF). Pu-UiO-66 expands the established UiO-66 series, which includes transition metal, lanthanide, and early actinide elements in the hexanuclear nodes. The thermal stability and porosity of Pu-UiO-66 were experimentally determined, and multifaceted computational methods were used to corroborate experimental values, examine inherent defects in the framework, decipher spectroscopic signatures, and elucidate the electronic structure. The crystallization of a plutonium chain side product provides direct evidence of the competition that occurs between modulator and linker in MOF syntheses. Ultimately, the synthesis of Pu-UiO-66 demonstrates adept control of Pu(IV) coordination under hydrolysis-prone conditions, provides an opportunity to extend trends across isostructural UiO-66 frameworks, and serves as the foundation for future plutonium MOF chemistry.

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

Atomistic Simulations of Thermal and Chemical Expansions of PrNi x Co 1‐x O 3‐δ Accelerated by Machine Learning Potentials

The electrodes and solid-state electrolytes in protonic ceramic electrochemical cells (PCECs) experience significant lattice expansions when exposed to high steam concentrations at elevated temperatures. In this paper, phonon calculations based on a new machine learning potential (MLP) are employed to elucidate the volume expansions of the proton-conducting PrNi x Co 1-x O 3-δ (PNC) lattices, manifested under a combined influence of oxygen vacancies (V$^{\cdot\cdot}_O$ ) and proton uptake (OH$^{\cdot}_O$ ) in the bulk at varying Ni/Co occupancies. It is revealed that the Ni/Co occupancy contributes to thermal and chemical expansions differently, where thermal expansions are related to Co occupancy. In contrast, chemical expansions are more closely associated with the Ni occupancy. Both V$^{\cdot\cdot}_O$ and OH$^{\cdot}_O$ lead to higher thermal expansions when compared to the pristine PNC. The temperature increase will negatively impact the hydration-induced chemical expansions. For combined thermal and chemical expansions, it is predicted that the strategies that boost the PCEC's electrochemical performance may harm the electrode–electrolyte interfacial stability, when the Ni occupancy is high, due to severe chemical expansions. Mitigating chemical expansions of the Ni-abundant PNC will benefit the interfacial stability. Finally, the presented computational methods for phonon calculations, based on emerging machine learning interatomic potential techniques are anticipated to have a lasting impact on future PCEC development.

computational prediction↗
DOE OSTI2025

Enhanced polymorph metastability drives glycine nucleation in aqueous salt solutions

Crystal nucleation from aqueous solutions influences countless geological, biochemical, astrophysical, environmental, and materials science–related phenomena, including ice formation, the manufacturing of active pharmaceutical ingredients, development of diseases such as Alzheimer’s and the origin of life itself. Understanding and controlling nucleation is essential for designing materials with specific properties, developing strategies to inhibit or promote crystallization in various contexts and preventing pathological aggregation in neurodegenerative diseases. Similar to the protein structure prediction problem—where a single amino acid sequence can in theory adopt one most stable conformation but in practice may sample multiple competing conformations—crystal nucleation faces a parallel challenge: the same chemical species can form diverse polymorphs under different environmental conditions (e.g., temperature, pressure, solvent). Each polymorph presents its own set of physical and chemical properties, highlighting the importance of understanding and controlling polymorph selection in fields ranging from pharmaceuticals to materials design. Despite advances in experimental and computational methods for studying phase transitions and polymorph stability, nucleation remains challenging due to its nanoscale nature. Furthermore, in practical settings, salts and impurities can further influence crystal nucleation in diverse contexts, from scaling in pipelines and desalination plants to the durability of concrete and the efficiency of battery materials. This can lead to the formation of polymorphs that may differ from the most stable phase in pure solutions. Or, even though the final structure might appear same irrespective of whether the environment contained impurities or not, the mechanism through which it was formed might be completely different and not intuitive.

Wang, Ruiyu [University of Maryland, College Park,↗
DOE OSTI2020

2018 LDRD Annual Report (Argonne National Laboratory)

Argonne National Laboratory’s Laboratory Directed Research and Development (LDRD) program encourages the development of novel technical concepts, enhances the Laboratory’s research and development (R&D) capabilities, and enables pursuit of strategic laboratory goals. Argonne’s LDRD projects are proposal based and peer reviewed, supporting ideas that require advanced exploration so they can be sufficiently developed to pursue support through normal programmatic channels. Among the aims of the projects supported by the LDRD program are the establishment of engineering proofs of principle, assessment of design feasibility for prospective facilities, development of instrumentation or computational methods or systems, and discoveries in fundamental science and exploratory development. All LDRD projects have demonstrable ties to one or more of the science, energy, environment, and national security missions of the U.S. Department of Energy (DOE) and its National Nuclear Security Administration (NNSA), and many are also relevant to the missions of other federal agencies that sponsor work at Argonne. A natural consequence of the more “applied” type projects is their concurrent relevance to industry. The LDRD program is managed in overarching portfolios, each containing multiple projects each fiscal year. The LDRD Prime portfolio is further divided into strategic focus areas aligned with Argonne’s strategic plan. The largest component of Argonne’s program is LDRD Prime, which emphasizes R&D explicitly aligned with Laboratory major initiatives in support of Argonne’s strategic plan. The choice of Focus Areas under the LDRD Prime component reflects the major initiatives; the state of development of relevant technical fields; the potential value of advancing those fields to DOE/NNSA and the nation; and the compatibility of the fields with existing facilities, capabilities, and staff expertise at Argonne. Focus Areas with projects that ended in FY18 are: Advanced Computing, Biological and Environmental Science Capability Development, Energy Manufacturing Science and Engineering, Hard X-ray Sciences, Materials and Chemistry, Securing Energy and Critical Resources, and The Universe as Our Laboratory (ULab).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Application of ESMACS binding free energy protocols to diverse datasets: Bromodomain-containing protein 4

As the application of computational methods in drug discovery pipelines becomes more widespread it is increasingly important to understand how reproducible their results are and how sensitive they are to choices made in simulation setup and analysis. Here we use ensemble simulation protocols, termed ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), to investigate the sensitivity of the popular molecular mechanics Poisson-Boltzmann surface area (MMPBSA) methodology. Using the bromodomain-containing protein 4 (BRD4) system bound to a diverse set of ligands as our target, we show that robust rankings can be produced only through combining ensemble sampling with multiple trajectories and enhanced solvation via an explicit ligand hydration shell.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Development of MOSCATO: A CFD-Level Electrochemistry and Corrosion Simulator for Molten Salt Systems

For both coolant and fueled variants of molten salt reactors (MSRs), the corrosion of structural materials is a significant challenge. The corrosion stems from chemical and electrochemical reactions initiated by fissile material, fission products, and impurities in the salt. Lower-fidelity models rely on empirical correlations for mass transfer, simplified lumped temperature profiles, and similar assumptions. They do not capture detailed spatial variations in complex geometries, creating the need for high-fidelity modeling to bridge this gap.As we approach the demonstration and possible deployment of MSRs in this decade, the development of a high-fidelity, high-performance simulator becomes imperative. To simulate the complex electrochemical environment and corrosion within molten salt systems, we have developed the Molten Salt Chemistry And TranspOrt (MOSCATO) code. This endeavor is comprised of three essential components. First, mass transfer equations are coupled with the Navier-Stokes equations in order to account for the transport of species in the salt. Second, the diffusion of alloy constituents, such as Cr, Fe, Ni, etc. is simulated within the structural metals. Third, the alloy and salt domains are coupled to account for the heterogeneous chemical and electrochemical reactions that occur at the salt-alloy interface.MOSCATO manages all three components within the framework of the highly scalable, open-source spectral element method computational fluid dynamics code Nek5000/NekRS. This integration enables MOSCATO to harness the immense computational power of modern high-performance computing resources, ensuring both high fidelity and computational speed.In addition to code development, we have initiated a comprehensive verification and validation campaign, utilizing data from diverse sources. First, MOSCATO's electrochemical solver was verified with reference numerical data. Then validation occurred against experiments: one of a thermal galvanic cell and the other for corrosion in flowing molten salt of FLiNaK (LiF-NaF-KF). This campaign verified and validated MOSCATO as a reliable tool for simulating electrochemical environments and corrosion in molten salt systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

An Overview of the Computational Physics and Methods Group at Los Alamos National Laboratory

CCS Division was formed to strengthen the visibility and impact of computer science and computational physics research on strategic directions for the Laboratory. Both computer science and computational science are now central to scientific discovery and innovation. They have become indispensable tools for all other scientific missions at the Laboratory. CCS Division forms a bridge between external partners and Laboratory programs, bringing new ideas and technologies to bear on today’s important problems and attracting high-quality technical staff members to the Laboratory. The Computational Physics and Methods Group CCS-2 conducts methods research and develops scientific software aimed at the latest and emerging HPC systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Dealloying, Microstructure and the Corrosion/Protection of Cast Magnesium Alloys

The purpose of this project was to develop a greater understanding of micro-galvanic corrosion effects in cast magnesium alloys using both experimental and computational methods. Experimental accomplishments have been made in the following areas of interest: characterization, aqueous free-corrosion, atmospheric corrosion, ionic liquid dissolution, rate kinetics of oxide dissolution, and coating investigation. Commercial alloys (AZ91D, AM60, and AZ31B), binary-phase alloys (αMg-2at.%Al, αMg-5at.%Al, and Mg-8at.%Al), and component phases (Mg, Al, β-Mg, β-1%Zn, MnAl3) were obtained and characterized using energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), and scanning electron microscopy (SEM). Full immersion in aqueous chloride was used to characterize the corrosion behavior of alloys. Rotating disc electrodes (RDEs) were used to observe accelerated long-term corrosion behavior. Al surface redistribution for freely corroded samples was analyzed using SEM, EDS, and lithium underpotential deposition (Li UPD). Atmospheric corrosion was observed using contact angle evolution, overnight pH monitoring, and surface pH evolution studies. Ionic liquid corrosion characterization was performed using linear sweep voltammetry and potentiostatic dissolution in 150° choline chloride-urea (cc-urea). Two surface coatings were investigated: (1) Li-carbonate and (2) cc-urea. Li-carbonate coatings were characterized using X-ray photoelectron spectroscopy (XPS), SEM, and aqueous free corrosion potential monitoring. Hydrophobic cc-urea coatings were characterized using contact angle measurements and electrochemical impedance spectroscopy. Oxide dissolution rate kinetics were studied using inductively coupled plasma mass spectroscopy (ICP-MS). Computational accomplishments have been made through the development of Kinetic Monte Carlo (KMC) simulations which model time- and composition-dependent effects on the microstructure due to spatial redistribution of alloying elements during corrosion.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Minor Isozymes Tailor Yeast Metabolism to Carbon Availability

ABSTRACT Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 ( ACO2 ) and pyruvate kinase 2 ( PYK2 ). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2 ’s deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroring PYK2 ’s allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states. IMPORTANCE Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗