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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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DOE OSTI2026

A comprehensive first-principles study of the effects of the exchange-correlation functional and magnetism on defect and diffusion properties of the CoCrNi medium-entropy alloy

The present work is a novel, systematic study of the effect of density functional theory input parameters on the vacancy formation energy (VFE), migration barrier for diffusion, and electronic structure for each element in the CoCrNi medium-entropy alloy (MEA). In particular, the novelties include: (1) calculating the aforementioned properties of Co, Cr, or Ni, in the CoCrNi MEA using magnetic and non-magnetic states, and two versions of the generalized gradient approximation: Perdew, Burke, and Ernzerhof (PBE) and the PBE version for solids (PBEsol), and (2) a detailed comparison of 0 K activation energy to experimental creep activation energies. First-principles calculations at 0 K are performed using the Vienna ab-initio simulation package. Special quasirandom structures (SQS) and Widom-type substitution are employed. For each element, Co, Cr, or Ni, non-magnetic calculations result in a higher VFE and larger range of calculated values for the configurations studied. The averaged migration barrier is the highest for Co in the CoCrNi for three of four sets of calculation parameters in the configurations studied. Finally, the results indicate that the average 0 K activation energy for diffusion makes up 70–80% of the experimental creep activation energy, depending on the exchange-correlation functional employed.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Structural and Functional Survey of Environmental Aminoglycoside Acetyltransferases Reveals Functionality of Resistance Enzymes

Aminoglycoside N-acetyltransferases (AACs) confer resistance against the clinical use of aminoglycoside antibiotics. The origin of AACs can be traced to environmental microbial species representing a vast reservoir for new and emerging resistance enzymes, which are currently undercharacterized. In this work, we performed detailed structural characterization and functional analyses of four metagenomic AAC (meta-AACs) enzymes recently identified in a survey of agricultural and grassland soil microbiomes (Forsberg et al. Nature 2014, 509, 612). These enzymes are new members of the Gcn5-Related-N-Acetyltransferase superfamily and confer resistance to the aminoglycosides gentamicin C, sisomicin, and tobramycin. Moreover, the meta-AAC0020 enzyme demonstrated activity comparable with an AAC(3)-I enzyme that serves as a model AAC enzyme identified in a clinical bacterial isolate. The crystal structure of meta-AAC0020 in complex with sisomicin confirmed an unexpected AAC(6') regiospecificity of this enzyme and revealed a drug binding mechanism distinct from previously characterized AAC(6') enzymes. Together, our data highlights the presence of highly active antibiotic-modifying enzymes in the environmental microbiome and reveals unexpected diversity in substrate specificity. These observations of additional AAC enzymes must be considered in the search for novel aminoglycosides less prone to resistance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Water Observations of Flow/No-Flow for the East-Taylor Watershed, Colorado (June-July 2025 and 2026)

This dataset provides multi-year, ground-truth visual observations of surface water flow/no-flow conditions within the East-Taylor Watershed, Colorado, collected during June and July of 2025 and 2026. In June and July 2025, on-the-ground visual observations of flow/no-flow were collected as part of the Watershed Function Scientific Focus Area (SFA) and Rocky Mountain Biological Laboratory (RMBL) Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign (further details are provided within the CHESS Project Description). We obtained 377 water observations of flow/no-flow within the East-Taylor Watershed, Colorado. These ground-truth observations were collected to validate classification maps from remote sensing data and model results within the East-Taylor Watershed. In 2025, flow/no-flow measurements were collected using a field-based app for the CHESS Campaign (Zerion iForm). Within the field app, a water observation form was created to collect coordinates and metadata about the observation. Information collected for the water observation points included information about visually-assessed streamflow presence/absence (standard question obtained from Colorado State University’s StreamTracker project), flow estimate, stream or ponded area width, canopy cover, manganese films, iron seeps, and beaver activity. For 2025 water observations, this dataset contains: (1) a data file with the water observations and coordinates (2025_Water_Observations.csv); (2) a Keyhole Markup Language Zipped (KMZ) with the water observation locations and metadata (2025_Water_Observations_Locations.kmz); (3) photos (.jpg and .jpeg) of the water observation points, organized by location, contained within 2025_Water_Observations_FieldPhotographs.zip file; and (4) water observation protocols and figures (2025_Water_Observation_Protocols.pdf). In June and July 2026, on-the-ground visual observations of flow/no-flow were collected as part of the Watershed Function SFA project. We obtained 365 water observations of flow/no-flow within the East-Taylor Watershed, Colorado. The 2026 observations focused on collecting repeat measurements at the 2025 flow/no-flow observation locations conducted as part of the CHESS campaign. These ground-truth observations were collected to understand differences in flow/no-flow in 2026, given the unprecedented 2026 drought in Colorado. In 2026, flow/no-flow measurements were collected using ArcGIS (Geographic Information System) Survey123. Within the field app, a water observation form was created to collect coordinates and metadata about the observation. Information collected for the water observation points included repeat information from the 2025 water observation effort, including visually-assessed streamflow presence/absence (standard question obtained from Colorado State University’s StreamTracker project), flow estimate, stream or ponded area width, canopy cover, manganese films, iron seeps, beaver activity, and a new metadata component of estimated stream depth (for select locations). For 2026 water observations, this dataset contains: (1) a data file with the water observations and coordinates (2026_Water_Observations.csv); (2) a Keyhole Markup Language Zipped (KMZ) with the water observation locations and metadata (2026_Water_Observations_Locations.kmz); (3) photos (.jpg) of the water observation points, organized by location, contained within 2026_Water_Observations_FieldPhotographs.zip file; and (4) water observation protocols and figures (2026_Water_Observation_Protocols.pdf). For 2025 and 2026 water observations, this dataset contains: (1) a location metadata file (locations.csv); (6) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and (7) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. 2026-09-02: This dataset was updated to include 2026 water observation measurements. The 2025 observation files were also updated to ensure a consistent file naming convention across water observation years.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Enhancing Cycling Stability of Lithium Metal Batteries by a Bifunctional Fluorinated Ether

Abstract The lifespan of lithium (Li) metal batteries (LMBs) can be greatly improved by the formation of inorganic‐rich electrode‐electrolyte interphases (EEIs) (including solid‐electrolyte interphase on anode and cathode‐electrolyte interphase on cathode). In this work, a localized high‐concentration electrolyte containing lithium bis(fluorosulfonyl)imide (LiFSI) salt, 1,2‐dimethoxyethane (DME) solvent and 1,2‐bis(1,1,2,2‐tetrafluoroethoxy)ethane (BTFEE) diluent is optimized. BTFEE is a fluorinated ether with weakly‐solvating ability for LiFSI so it also acts as a co‐solvent in this electrolyte. It can facilitate anion decomposition at electrode surfaces and promote the formation of more inorganic‐rich EEI layers. With an optimized molar ratio of LiFSI:DME:BTFEE = 1:1.15:3, LMBs with a high loading (4 mAh cm −2 ) lithium nickel manganese cobalt oxide (LiNi 0.8 Mn 0.1 Co 0.1 ) cathode can retain 80% capacity in 470 cycles when cycled in a voltage range of 2.8–4.4 V. The fundamental understanding on the functionality of BTFEE revealed in this work provides new perspectives on the design of practical high‐energy density battery systems.

25 ENERGY STORAGE↗
DOE OSTI2015

SU-C-BRA-06: Developing Clinical and Quantitative Guidelines for a 4DCT-Ventilation Functional Avoidance Clinical Trial

Purpose: 4DCT-ventilation is an exciting new imaging modality that uses 4DCTs to calculate lung ventilation. Because 4DCTs are acquired as part of routine care, calculating 4DCT-ventilation allows for lung function evaluation without additional cost or inconvenience to the patient. Development of a clinical trial is underway at our institution to use 4DCT-ventilation for thoracic functional avoidance with the idea that preferential sparing of functional lung regions can decrease pulmonary toxicity. The purpose of our work was to develop the practical aspects of a 4DCT-ventilation functional avoidance clinical trial including: 1.assessing patient eligibility 2.developing trial inclusion criteria and 3.developing treatment planning and dose-function evaluation strategies. Methods: 96 stage III lung cancer patients from 2 institutions were retrospectively reviewed. 4DCT-ventilation maps were calculated using the patient’s 4DCTs, deformable image registrations, and a density-change-based algorithm. To assess patient eligibility and develop trial inclusion criteria we used an observer-based binary end point noting the presence or absence of a ventilation defect and developed an algorithm based on the percent ventilation in each lung third. Functional avoidance planning integrating 4DCT-ventilation was performed using rapid-arc and compared to the patient’s clinically used plan. Results: Investigator-determined clinical ventilation defects were present in 69% of patients. Our regional/lung-thirds ventilation algorithm identified that 59% of patients have lung functional profiles suitable for functional avoidance. Compared to the clinical plan, functional avoidance planning was able to reduce the mean dose to functional lung by 2 Gy while delivering comparable target coverage and cord/heart doses. Conclusions: 4DCT-ventilation functional avoidance clinical trials have great potential to reduce toxicity, and our data suggest that 59% of lung cancer patients have lung function profiles suitable for functional avoidance. Our study used a retrospective evaluation of a large lung cancer patient database to develop the practical aspects of a 4DCT-ventilation functional avoidance clinical trial. (R.C., E.C., T.G.), NIH Research Scientist Development Award K01-CA181292 (R.C.), and State of Colorado Advanced Industries Accelerator Grant (Y.V.)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

An In Situ , multi-electrode electrochemical method to assess the open circuit potential corrosion of Cr in unpurified molten FLiNaK

An in situ electrochemical method to investigate the time-dependent spontaneous corrosion of pure Cr in an unpurified LiF-NaF-KF eutectic salt at 600 °C was developed. A multi-electrode electrochemical cell and a dual-electrode method were utilized to detect Cr(II) and Cr(III) ions on a platinum working electrode using cyclic voltammetry as a function of exposure time and compensating for the distance from the Cr electrode. XRD was performed to characterize the crystalline composition of the exposed FLiNaK and the salt films formed on the working electrodes. The fate of all electro-oxidized Cr must be accounted for to quantify corrosion rates. The concentrations of Cr(II) and Cr(III) species obtained were compared with the gravimetric mass change and ICP-OES analysis of the residual salt. Finally, outcomes, difficulties, and limitations are discussed.

ICP-OES↗
DOE OSTI2012

Coherent control and time-dependent density functional theory: Towards creation of wave packets by ultrashort laser pulses

Explicitly time-dependent density functional theory (TDDFT) is a formally exact theory, which can treat very large systems. However, in practice it is used almost exclusively in the adiabatic approximation and with standard ground state functionals. Therefore, if combined with coherent control theory, it is not clear which control tasks can be achieved reliably, and how this depends on the functionals. In this paper, we continue earlier work in order to establish rules that answer these questions. Specifically, we look at the creation of wave packets by ultrashort laser pulses that contain several excited states. We find that (i) adiabatic TDDFT only works if the system is not driven too far from the ground state, (ii) the permanent dipole moments involved should not differ too much, and (iii) these results are independent of the functional used. Additionally, we find an artifact that produces fluence-dependent excitation energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Merging symmetry projection methods with coupled cluster theory: Lessons from the Lipkin model Hamiltonian

Coupled cluster and symmetry projected Hartree-Fock are two central paradigms in electronic structure theory. However, they are very different. Single reference coupled cluster is highly successful for treating weakly correlated systems, but fails under strong correlation unless one sacrifices good quantum numbers and works with broken-symmetry wave functions, which is unphysical for finite systems. Symmetry projection is effective for the treatment of strong correlation at the mean-field level through multireference non-orthogonal configuration interaction wavefunctions, but unlike coupled cluster, it is neither size extensive nor ideal for treating dynamic correlation. We here examine different scenarios for merging these two dissimilar theories. We carry out this exercise over the integrable Lipkin model Hamiltonian, which despite its simplicity, encompasses non-trivial physics for degenerate systems and can be solved via diagonalization for a very large number of particles. We show how symmetry projection and coupled cluster doubles individually fail in different correlation limits, whereas models that merge these two theories are highly successful over the entire phase diagram. Despite the simplicity of the Lipkin Hamiltonian, the lessons learned in this work will be useful for building an ab initio symmetry projected coupled cluster theory that we expect to be accurate in the weakly and strongly correlated limits, as well as the recoupling regime.

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

Evaluating the Influence of Plants on Hydrologic Cycling: Quantifying and Validating the Role of Plant Processes and Stomatal Conductance (Final Report)

Plants can exert strong controls on water cycling, both locally and across the globe. The impacts are driven by how plants control the exchange of water and energy between the land and the atmosphere, which in turn are influenced by how plants photosynthesize and grow (e.g. biogeochemical cycling). Our team has shown previously that plant responses to increasing CO 2 in the atmosphere can influence the water cycle through changes in rainfall and the amount of water on land with implications for drought. Plant responses can also alter both average and extreme flow of water in rivers, with implications for freshwater availability and flood frequency. Although the impact of plant responses on water cycling has been demonstrated to exist, significant uncertainty remains in our understanding of the magnitude and form of the plant responses themselves, as well as their ultimate impact on water availability for people and ecosystems. In this project we worked to quantify the role of plant processes in regulating water cycling (e.g., precipitation, evapotranspiration, runoff, droughts, etc.). We addressed the following objectives: quantify the control of physiological vs. radiative effects on water cycling across many coupled models in idealized and realistic scenarios, as well as to quantify how our assumptions about leaf-level processes (e.g. coupling between stomatal conductance and photosynthesis), and organism-level processes (e.g. leaf area response to high CO 2 ) contributed to uncertainties and biases in water cycling. We focused on the impact of these processes on water cycling to assess the sensitivities of precipitation, evapotranspiration, and runoff to model assumptions about plant processes at the leaf, organism, and community level. As part of this project we used measurements of stable carbon isotopes to generate observationally based estimates of plant functioning during the historical period. This work is directly relevant to DOE’s primary scientific research questions in the RGMA topic area (b) on Biogeochemical processes, feedbacks, and interactions in the Earth system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Merging symmetry projection methods with coupled cluster theory: Lessons from the Lipkin model Hamiltonian

Coupled cluster and symmetry projected Hartree-Fock are two central paradigms in electronic structure theory. However, they are very different. Single reference coupled cluster is highly successful for treating weakly correlated systems but fails under strong correlation unless one sacrifices good quantum numbers and works with broken-symmetry wave functions, which is unphysical for finite systems. Symmetry projection is effective for the treatment of strong correlation at the mean-field level through multireference non-orthogonal configuration interaction wavefunctions, but unlike coupled cluster, it is neither size extensive nor ideal for treating dynamic correlation. We here examine different scenarios for merging these two dissimilar theories. We carry out this exercise over the integrable Lipkin model Hamiltonian, which despite its simplicity, encompasses non-trivial physics for degenerate systems and can be solved via diagonalization for a very large number of particles. We show how symmetry projection and coupled cluster doubles individually fail in different correlation limits, whereas models that merge these two theories are highly successful over the entire phase diagram. In conclusion, despite the simplicity of the Lipkin Hamiltonian, the lessons learned in this work will be useful for building an ab initio symmetry projected coupled cluster theory that we expect to be accurate in the weakly and strongly correlated limits, as well as the recoupling regime.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Cation-size mismatch as a predictive descriptor for structural distortion, configurational disorder, and valence-band splitting in II-IV-N2 semiconductors

The II-IV-N2 class of heterovalent ternary nitrides has gained significant interest as alternatives to the III-nitrides for electronic and optoelectronic applications. In this study, we apply first-principles calculations based on density functional theory to systematically investigate the effects of structural distortions due to cation size mismatch on the configurational disorder of the cation sublattice and the valence band structure in this class of materials. We find that larger size mismatch between the group-II and the group-IV cations results in stronger lattice distortions from the ideal hexagonal ratio, which in turn inhibits the propensity of these materials toward octet-rule violating cation disorder. We also demonstrate that the formation energy of a single cation antisite pair, which is fast and simple to calculate, is a strong indicator of a material's propensity toward disorder. Furthermore, the breaking of in-plane symmetry leads to a splitting of the top three valence bands at Γ, which is also directly related to the magnitude of structural distortions. Our work demonstrates that the structural and functional properties of the II-IV-N2 materials can be finely tuned through controllable structural distortions that stem from the choice of cations.

Kute, Malhar (ORCID:0000000214364564)↗
DOE OSTI2021

Localized Active Space Pair-Density Functional Theory

Accurate quantum chemical methods for the prediction of spin-state energy gaps for strongly correlated systems are computationally expensive and scale poorly with the size of the system. This makes calculations for many experimentally interesting molecules impractical even with abundant computational resources. Previous work has shown that the localized active space (LAS) self-consistent field (SCF) method can be an efficient way to obtain multiconfiguration SCF wave functions of comparable quality to the corresponding complete active space (CAS) ones. To obtain quantitative results, a post-SCF method is needed to estimate the complete correlation energy. One such method is multiconfiguration pair-density functional theory (PDFT), which calculates the energy based on the density and on-top pair density obtained from a multiconfiguration wave function. In this work, we introduce localized-active-space PDFT, which uses a LAS wave function for subsequent PDFT calculations. The method is tested by computing spin-state energies and gaps in conjugated organic molecules and a bimetallic compound and comparing to the corresponding CAS-PDFT values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Ensuring thermodynamic consistency with invertible coarse-graining

Coarse-grained models are a core computational tool in theoretical chemistry and biophysics. A judicious choice of a coarse-grained model can yield physical insights by isolating the essential degrees of freedom that dictate the thermodynamic properties of a complex, condensed-phase system. The reduced complexity of the model typically leads to lower computational costs and more efficient sampling compared with atomistic models. Designing “good” coarse-grained models is an art. Generally, the mapping from fine-grained configurations to coarse-grained configurations itself is not optimized in any way; instead, the energy function associated with the mapped configurations is. In this work, we explore the consequences of optimizing the coarse-grained representation alongside its potential energy function. We use a graph machine learning framework to embed atomic configurations into a low-dimensional space to produce efficient representations of the original molecular system. Because the representation we obtain is no longer directly interpretable as a real-space representation of the atomic coordinates, we also introduce an inversion process and an associated thermodynamic consistency relation that allows us to rigorously sample fine-grained configurations conditioned on the coarse-grained sampling. We illustrate that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide.

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

NEON AOP Survey of Upper East River CO Watersheds: Waveform LiDAR Binary Data

The waveform Light Detection and Ranging (LiDAR) data in this package were generated through a National Ecological Observatory Network Airborne Observation Platform (NEON AOP) acquisition over watersheds of interest surrounding Crested Butte, Colorado. The remote sensing imagery acquired by the NEON AOP supports an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in a snow-dominated headwater environment. These waveform LiDAR data enable spatially continuous estimation of vegetation structure parameters to facilitate analyses of the major environmental drivers of structural and compositional variability. The package contains 97 compressed file archives in 7-zip (.7z) format, each corresponding to one acquisition flightpath. Within each .7z archive is a set of constituent files describing properties of the LiDAR waveforms, such as return intensity, geolocation, outgoing pulse and other behavior of the sensor and signals. Once downloaded, the files must first be unzipped using the widely distributed command-line software utility 7z, using the command '7z x \[filename\].7z \[target_directory\]'. All files within the .7z archives can be opened in IDL, MatLab, or the open-source R statistical computing environment. Further details about the data package are in the attached user guide (neon_aop_crbu_waveformlidar_userguide.pdf). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2019

Learning nonlinear level sets for dimensionality reduction in function approximation

We developed a Nonlinear Level-set Learning (NLL) method for dimensionality reduction in high-dimensional function approximation with small data. This work is motivated by a variety of design tasks in real-world engineering applications, where practitioners would replace their computationally intensive physical models (e.g., high-resolution fluid simulators) with fast-to-evaluate predictive machine learning models, so as to accelerate the engineering design processes. There are two major challenges in constructing such predictive models: (a) high-dimensional inputs (e.g., many independent design parameters) and (b) small training data, generated by running extremely time-consuming simulations. Thus, reducing the input dimension is critical to alleviate the over-fitting issue caused by data insufficiency. Existing methods, including sliced inverse regression and active subspace approaches, reduce the input dimension by learning a linear coordinate transformation; our main contribution is to extend the transformation approach to a nonlinear regime. Specifically, we exploit reversible networks (RevNets) to learn nonlinear level sets of a high-dimensional function and parameterize its level sets in low-dimensional spaces. A new loss function was designed to utilize samples of the target functions' gradient to encourage the transformed function to be sensitive to only a few transformed coordinates. The NLL approach is demonstrated by applying it to three 2D functions and two 20D functions for showing the improved approximation accuracy with the use of nonlinear transformation, as well as to an 8D composite material design problem for optimizing the buckling-resistance performance of composite shells of rocket inter-stages.

Zhang, Guannan↗
DOE OSTI2017

eQE: An open‐source density functional embedding theory code for the condensed phase

Abstract In this work, we present the main features and algorithmic details of a novel implementation of the frozen density embedding (FDE) formulation of subsystem density functional theory (DFT) that is specifically designed to enable ab initio molecular dynamics (AIMD) simulations of large‐scale condensed‐phase systems containing 1000s of atoms. This code (available at http://eqe.rutgers.edu ) has been given the moniker of embedded Quantum ESPRESSO (eQE) as it is a generalization of the open‐source Quantum ESPRESSO (QE) suite of programs. The strengths of eQE reside in a hierarchical parallelization scheme that allows for an efficient and fully self‐consistent treatment of the electronic structure (via the addition of an additional DIIS extrapolation layer) while simultaneously exploiting the inherent symmetries and periodicities in the system (via sampling of subsystem‐specific first Brillouin zones and utilization of subsystem‐specific basis sets). While bulk liquids and molecular crystals are two classes of systems that exemplify the utility of the FDE approach (as these systems can be partitioned into weakly interacting subunits), we show that eQE has significantly extended this regime of applicability by outperforming standard semilocal Kohn–Sham DFT (KS‐DFT) for large‐scale heterogeneous catalysts with quite different layer‐specific electronic structure and intrinsic periodicities. eQE features very favorable strong parallel scaling for a model system of bulk liquid water composed of 256 water molecules, which allows for a significant decrease in the overall time to solution when compared to KS‐DFT. We show that eQE achieves speedups greater than one order of magnitude ( ) when performing AIMD simulations of such large‐scale condensed‐phase systems as: (1) molecular liquids via bulk liquid water represented by 1024 independent water molecules (3072 atoms with a 25.3× speedup over KS‐DFT), (2) polypeptide/biomolecule solvation via ( gly ) 6 solvated in (H 2 O) 395 (1230 atoms with a 38.6× speedup over KS‐DFT), and (3) molecular crystals via a 3 × 3 × 3 periodic supercell of pentacene (1940 atoms with a 12.0× speedup over KS‐DFT). These results represent a significant improvement over the current state‐of‐the‐art and now enable subsystem DFT‐based AIMD simulations of realistically sized condensed‐phase systems of interest throughout chemistry, physics, and materials science.

Genova, Alessandro↗
DOE OSTI2018

Isoprenylcysteine carboxylmethyltransferase regulates ovarian cancer cell response to chemotherapy and Ras activation

Highlights: • Icmt upregulation is a common feature in ovarian cancer. • Icmt expression level varies in ovarian cancer tissues and cell lines. • Icmt expression is associated with ovarian cancer cell chemoresistance. • Icmt inhibition sensitizes ovarian cancer to chemotherapy in vitro and in vivo. • Icmt inhibition suppresses Ras and its downstream signaling pathways. Inhibition of isoprenylcysteine carboxylmethyltransferase (Icmt), which catalyzes the final step of oncoproteins' prenylation, targets growth and survival of various cancers. In this work, we systematically studied the expression, functions and molecular signaling of Icmt in ovarian cancer. We show that the upregulation of Icmt expression is a common feature in patients with epithelial ovarian cancer regardless of age and disease stage. In line with the observations in ovarian cancer patients, a panel of epithelial ovarian cancer cell lines also demonstrates the significant increase on Icmt transcript and protein levels than normal ovarian epithelial cells. In addition, ovarian cancer cell lines with higher Icmt levels are more resistant to chemotherapeutic agents. We further show that Icmt inhibition by siRNA or inhibitor cysmethynil suppresses growth and induces apoptosis in ovarian cancer cells. Importantly, Icmt inhibition significantly augments chemotherapeutic agent's efficacy in vitro and in vivo, demonstrating the translational potential of Icmt inhibition in ovarian cancer. Mechanistically, we show that Ras activation is a critical effector of Icmt in ovarian cancer cells. Using cell culturing system, mouse model and patient samples, our work is the first to demonstrate the essential roles of Icmt in ovarian cancer via Ras signaling, particularly on its response to chemotherapy. Our findings suggest that Icmt inhibition is a promising therapeutic strategy to overcome chemoresistance in ovarian cancer, in particular, those patients with high Icmt expression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Emergent Magnetic States and Tunable Exchange Bias at 3d Nitride Heterointerfaces

Abstract Interfacial magnetism stimulates the discovery of giant magnetoresistance (MR) and spin–orbital coupling across the heterointerfaces, facilitating the intimate correlation between spin transport and complex magnetic structures. Over decades, functional heterointerfaces composed of nitrides have seldom been explored due to the difficulty in synthesizing high‐quality nitride films with correct compositions. Here, the fabrication of single‐crystalline ferromagnetic Fe 3 N thin films with precisely controlled thicknesses is reported. As film thickness decreases, the magnetization dramatically deteriorates, and the electronic state changes from metallic to insulating. Strikingly, the high‐temperature ferromagnetism is maintained in a Fe 3 N layer with a thickness down to 2 u.c. (≈8 Å). The MR exhibits a strong in‐plane anisotropy; meanwhile, the anomalous Hall resistivity reverses its sign when the Fe 3 N layer thickness exceeds 5 u.c. Furthermore, a sizable exchange bias is observed at the interfaces between a ferromagnetic Fe 3 N and an antiferromagnetic CrN. The exchange bias field and saturation moment strongly depend on the controllable bending curvature using the cylinder diameter engineering technique, implying the tunable magnetic states under lattice deformation. This work provides a guideline for exploring functional nitride films and applying their interfacial phenomena for innovative perspectives toward practical applications.

Jin, Qiao↗