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DOE OSTI2025

Multireference diffusion Monte Carlo reaches 2D materials

Abstract Quantum confinement in 2D materials strongly enhances electronic correlation effects. Therefore, predicting the properties of these unique materials, with both a high level of accuracy and computational efficiency, without relying on adjustable parameters or functionals, remains an outstanding theoretical challenge. The majority of theoretical studies are based on the approximations of density functional theory (DFT). The reliability of DFT predictions are heavily dependent on the choice of an approximated exchange-correlation functional. Here, we estimate the magnitude of impact of correlation on the total energy for the quintessential 2D material, graphene, by performing and comparing state-of-the-art selected CI and quantum Monte Carlo extrapolated calculations for a single unit cell at the$$\Gamma$$point. We demonstrate that Self-Healing Diffusion Monte Carlo (SHDMC) obtains a very compact, but high-quality wavefunction for this system that lacks the strong basis set dependence displayed by state of the art quantum chemistry methods. The SHDMC wavefunction is of higher quality compared to that obtained from sCI, in the same orbital basis, while being$$\sim$$ 1000 times smaller in terms of determinant count compared to sCI. We also demonstrate that extrapolating SHDMC results to the infinite determinant limit compares extremely well with complete basis set extrapolated sCI. Our work paves the way for future validation of SHDMC applied to challenging 2D materials.

Science & Technology - Other Topics↗

DOE OSTI2026

Iron surface corrosion in supercritical CO2 at atomic scale investigated by molecular dynamics simulations

Understanding the corrosion behavior of steels in supercritical carbon dioxide (S-CO2) is essential for ensuring the safe application of S-CO2 as a heat-transfer fluid in high-temperature energy systems, including advanced nuclear reactors. In this work, molecular dynamics (MD) simulations using ReaxFF potential are performed to explore the atomic-scale corrosion mechanisms of body-centered cubic iron (BCC-Fe) in S-CO2. The results show that CO2 molecules in S-CO2 decompose at the Fe surface, generating free C and O atoms that form Fe-C and Fe-O bonds and subsequently produce oxides and carbides. Concurrently, Fe atoms dissolve from the surface and diffuse into the S-CO2 region, resulting in interdiffusion of Fe, C and O atoms at the interface. The corrosion-layer thickness calculations show that high pressure and temperature induced by S-CO2 have stronger effects than surface orientation on the corrosion process. In addition, surface Fe atoms undergo substantial displacement under S-CO2 exposure, further accelerating corrosion. When a radiation-induced void is introduced near the Fe surface, the corrosion is enhanced. The void-matrix interface expands the reaction surface area and simultaneously induces corrosion reactions inside the bulk, resulting in a deeper penetration of C and O and thicker corrosion layers. All these results indicate that high-temperature, high-pressure and radiation-induced voids can seriously affect the corrosion of Fe in S-CO2, and must be considered to better use S-CO2 in nuclear facilities.

Li, Wenhua↗

DOE OSTI2025

Three-dimensional nucleation and growth of deformation twins in magnesium

At two-thirds the weight of aluminum, magnesium alloys have the potential to reduce the fuel consumption of transportation vehicles. These advancements depend on our ability to optimize the desirable versus undesirable effects of deformation twins, which are three-dimensional (3D) microstructural domains that form under mechanical stresses. Previously only characterized through surface or thin-film measurements, we here present 3D in situ characterization of deformation twinning inside an embedded grain over mesoscopic fields of view using dark-field x-ray microscopy supported by crystal plasticity finite element analysis. The results revealed the role of triple junctions on twin nucleation and the sequence and irregularity of twin growth and showed that twin-grain junctions, twin-twin junctions, and twin boundaries were the sites of localized dislocation accumulation.

Lee, Sangwon [Univ. of Michigan, Ann Arbor, MI (Un↗

DOE OSTI2025

Quantum graph models for transport in filamentary switching

The formation of metallic nanofilaments bridging two electrodes across an insulator is a mechanism for resistive switching. Examples of such phenomena include atomic synapses, which constitute a distinct class of memristive devices the behavior of which is closely tied to the properties of the filament. Until recently, experimental investigation of the low-temperature regime and quantum transport effects has been limited. However, with growing interest in understanding the true impacts of the filament on device conductance, comprehending quantum effects has become crucial for quantum neuromorphic hardware. Here, we discuss quantum transport resulting from filamentary switching in a narrow region where the continuous approximation of the contact is not valid, and only a few atoms are involved. In this scenario, the filament can be represented by a graph depicting the adjacency of atoms and the overlap between atomic orbitals. Using the theory of quantum graphs with locally diffusive node scattering, we calculate the scattering amplitude of charge carriers on this graph and explore the interplay between filamentary formation and quantum transport effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Spinel high-entropy oxides (FeNiCrMnZnX) 3 O 4 (X = Al, mg) as anode materials for high-performance lithium-ion batteries

To address the high cost, cobalt dependency, and resource constraints typical of conventional high-entropy oxide (HEO) anodes, this study reports the successful synthesis of two Co-free, six-component spinel-type HEOs(FeNiCrMnZnAl) 3 O 4 (HEO-Al) and (FeNiCrMnZnMg) 3 O 4 (HEO-Mg), via a sol-gel method. The distinct effects of Al 3+ and Mg 2+ incorporation on the electrochemical performance and lithium storage kinetics were systematically investigated. XRD, Raman, and TEM characterizations confirm that both materials possess a pure spinel phase, uniform particle size, and homogeneous elemental distribution. Notably, electrochemical evaluations reveal that HEO-Al delivers a superior reversible capacity of 480.7 mAh g −1 after 100 cycles at 0.1 A g −1 , and maintains 354.5 mAh g −1 after 1000 long-term cycles at 1 A g −1 , significantly outperforming HEO-Mg. Kinetic analysis indicates that HEO-Al exhibits lower charge transfer resistance, a higher Li + diffusion coefficient, and a pseudocapacitive contribution of up to 82%. Furthermore, these findings demonstrate that Al substitution effectively optimizes the structural stability and lithium storage kinetics of Co-free HEOs, providing a viable strategy for designing low-cost, highly stable HEO anode systems.

Anode materials↗

DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗

DOE OSTI2026

Smart building HVAC control challenge: experience and solutions from the ADRENALIN project

A smart building HVAC control competition crowdsourced and compared algorithms on fair and equal ground using the standardized BOPTEST framework. The competition attracted 138 participants, but only 9% submitted valid solutions for the final stage, highlighting the complexity of advanced HVAC control design. The winning solutions showed significant potential to reduce energy use and cost by shifting demand, without compromising occupant comfort. Across scenarios, thermal energy cost reductions of 36–76% relative to a baseline, were achieved. In peak heat periods, the cost reduction leveraged limited energy use reduction (0–15%), but more significant energy price reduction (34–62%). This shows smart controls' ability to avoid as much as possible consumption during the morning peak hours, when spot prices are tendentially the highest. Hosting the competition has highlighted challenges in creating competitions that both are fair and promotes solutions that are transferable to real life implementation.

BOPTEST↗

DOE OSTI2023

Nonlinear Loss Engineering in Near‐Zero‐Index Bulk Materials

Transparent conducting oxides (TCOs) show unprecedented optical nonlinearities in the near infrared wavelength range, where the real part of their linear refractive index approaches zero. More specifically, the Kerr nonlinearities of these materials have sparked widespread attention due to their magnitude and speed. However, due to the absorptive nature of these nonlinear processes, it is of fundamental interest to further investigate the imaginary component of the nonlinear index. The present work studies the nonlinear optical absorption properties of aluminium‐doped zinc oxide (AZO) thin films in their near‐zero‐index (NZI) spectral window. It is found that the imaginary part of the refractive index is reduced under optical excitation such that the field penetration depth more than doubles. An optically induced shift of the NZI bandwidth of ≈120 nm for a pump intensity of 1.3 TW cm −2 is also demonstrated. Looking into the optically induced spectral redistribution of the probe signal, local net gain is recorded, which is ascribed to a nonlinear adiabatic energy transfer. The present study adds key information about the fundamental interplay between real and imaginary nonlinear indices in NZI media, while advancing parametric amplification as viable direction for loss compensation.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Temperature-Dependent Transport Characteristics of 2D MoS2 Channel FETs Grown Using Salt-Based Precursors

D Transition Metal Dichalcogenides (TMDs), particularly MoS2, are promising candidates for sub-10 nm Gate All Around (GAA) CMOS FETs. Salt-assisted Chemical Vapor Deposition (CVD) enable lateral MoS2 growth at atmospheric pressure and low temperatures. This work analyzes salt-based precursor-driven CVD-grown MoS2 FETs at various temperatures. MoS2 was grown using Ammonium Molybdate salt, sulfurized at 750∘C, and transferred onto p−Si3/SiO2 substrates. At room temperature, threshold voltage (VT) ranged from -35 V to -25 V, with a peak drain current of 1.2μA/μm. As temperature increased above 325K, VT shifted exponentially, and carrier mobility dropped significantly. At 400 K, the gate lost channel control, though gate leakage current remained low. These results are compared with non-salt-based MoS2 growth to assess salt precursor effects.Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doe-public-access-plan).

Jones, Andrew [ORNL] (ORCID:0009000233849687)↗

DOE OSTI2026

First-principles DFT modeling of nitrobenzene adsorption on the Ag(111) surface at varying monolayer coverages

Here, the adsorption behavior of nitrobenzene on the Ag(111) surface as a function of coverage is investigated using density functional theory. Adsorption energies and optimized geometries are analyzed together with isolated intermolecular interaction calculations and electronic structure analysis, including Bader charge partitioning and projected density of states, to disentangle the roles of adsorbate–surface bonding and through-space adsorbate–adsorbate interactions. At low and intermediate coverages (θ = 1/9 and θ = 2/9), bidentate adsorption at top sites is favored due to strong adsorbate–surface interactions, with additional stabilization at θ = 2/9 arising from favorable intermolecular separations. At higher coverage (θ = 1/3), bidentate adsorption is destabilized by strong intermolecular repulsion, and monodentate adsorption becomes energetically preferred as rotational freedom allows more favorable intermolecular spacing. Charge density differences, Bader charge, and density of states analyses show that charge transfer from the Ag surface is localized primarily on the nitro group and increases with coverage and adsorption denticity, although this increase does not directly correlate with adsorption strength at high coverage due to competing intermolecular interactions. These results demonstrate that surface coverage can induce a transition in preferred adsorption denticity driven by intermolecular interactions, highlighting the importance of adsorbate packing in organic molecule adsorption at metal interfaces.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Expanding the horizon of bio-naphtha beyond gasoline blend: property characterization and conversion opportunity assessment through technoeconomic and life-cycle analyses

Bio-naphtha, a common by-product of biorefineries, is expected to experience substantial growth in supply due to increasing demands for renewable diesel and synthetic aviation fuel (SAF). However, demand for bio-naphtha itself as a gasoline blendstock is limited because of the electrification of light-duty vehicles. This work investigated valorization opportunities for bio-naphtha from catalytic fast pyrolysis, hydrothermal liquefaction, Fischer–Tropsch synthesis, and hydrotreated esters and fatty acids pathways. These opportunities include producing polymer-grade olefin via steam cracking, SAF via steam cracking followed by olefin oligomerization, and renewable aromatics benzene, toluene, and xylene (BTX) and hydrogen via catalytic reforming. Process models were developed in Aspen Plus V14 and Aspen HYSYS V14 to calculate the mass and energy balances for each conversion step. Technoeconomic assessment and life-cycle analysis were conducted to evaluate the minimum fuel/product selling price, conversion cost, and life-cycle CO2 equivalent (CO2e) emission reduction. Technoeconomic assessment results suggest a minimum fuel/product selling price as low as $1.9/kg of olefins, $6.30/gal of gasoline-equivalent SAF, and $1.2/kg of BTX without any incentives. For all pathways, these prices are dominated by bio-naphtha feedstock costs, which account for at least 76% of the total cost. Compared with petroleum baselines, bio-naphtha-derived SAF, olefins, and BTX can achieve significant CO2e emission reductions from the use of renewable carbon resources. The results of life-cycle analysis and subsequent technoeconomic assessment, incorporating carbon credits, indicate the economic viability of using bio-naphtha for polymer-grade olefin and BTX production, with product costs comparable to market prices.

Xu, Shuang↗

DOE OSTI2025

Novel Zwitterionic Polyurethane-in-Salt Electrolytes with High Ion Conductivity, Elasticity, and Adhesion for High-Performance Solid-State Lithium Metal Batteries

This study presents a novel polymer-in-salt (PIS) zwitterionic polyurethane-based solid polymer electrolyte (zPU-SPE) that offers high ionic conductivity, strong interaction with electrodes, and excellent mechanical and electrochemical stabilities, making it promising for high-performance all solid-state lithium batteries (ASSLBs). The zPU-SPE exhibits remarkable lithium-ion (Li+) conductivity (3.7 × 10⁻⁴ S cm−1 at 25 °C), enabled by exceptionally high salt loading of up to 90 wt.% (12.6 molar ratio of Li salt to polymer unit) without phase separation. It addresses the limitations of conventional SPEs by combining high ionic conductivity with a Li+ transference number of 0.44, achieved through the incorporation of zwitterionic groups that enhance ion dissociation and transport. The high surface energy (338.4 J m−2) and elasticity ensure excellent adhesion to Li anodes, reducing interfacial resistance and ensuring uniform Li+ flux. When tested in Li||zPU||LiFePO₄ and Li||zPU||S/C cells, the zPU-SPE demonstrated remarkable cycling stability, retaining 76% capacity after 2000 cycles with the LiFePO4 cathode, and achieving 84% capacity retention after 300 cycles with the S/C cathode. Molecular simulations and a range of experimental characterizations confirm the superior structural organization of the zPU matrix, contributing to its outstanding electrochemical performance. The findings strongly suggest that zPU-SPE is a promising candidate for next-generation ASSLBs.

Wang, Kun↗

DOE OSTI2024

Circumventing data imbalance in magnetic ground state data for magnetic moment predictions

Abstract Magnetic materials play a crucial role in the transition to more sustainable forms of energy and electric vehicles. There is an anticipated shortage in magnetic materials in the future, and as a result there is an urgent need to discover and design new magnetic materials. Computational magnetic material design using density functional theory is daunting because of the challenge in identifying magnetic ground states from a combinatorially large set of possibilities. Machine learning offers a path forward by enabling efficient surrogate models that can more readily enumerate these states, but there is a dearth of training data available, and what is available tends to be imbalanced with too much non-magnetic data. In this work we show that the discrete and previously tackled data imbalance that exists at the level of the magnetic ordering leads to an imbalanced continuous distribution with many zeros when the data is unraveled at the atomic magnetic moment level, which subsequently leads to models with low accuracy for magnetic properties. We mitigate this by using a two-part model framework. Our scheme is able to classify atoms into magnetic and non-magnetic with an F1 score and Matthew’s correlation coefficient (MCC) of ~91% and then to provide an implicit embedding representation that maps directly onto the magnitude of the magnetic moment with a mean absolute error of 0.1 μ B . Beyond screening for new magnetic materials, we demonstrate an additional practical use case of our scheme: the provision of good initial guesses for magnetic moments in first-principles electronic relaxations. Such initialization is shown to lead to faster convergence to configurations that lie closer to the ground state.

Computer Science↗

DOE OSTI2026

Jumpstart Opportunities to Unleash Leadership in Energy Storage (JOULES)

Current-generation Li-ion batteries with cobalt- and nickel-containing cathodes and graphite anodes are approaching performance and cost limits. In this program, 24M Technologies, Inc. (24M) is teaming with the Massachusetts Institute of Technology (MIT) and University of Michigan (UM) to develop low cost and fast charging sodium metal batteries with good low-temperature performance and high energy density, building upon previous work performed under ARPA-E programs. Key achievements include optimization of solid electrolyte and anode current collector, optimized cathode active materials, development of high-performance electrolyte formulations, and integration of these components into full cells. The cell design incorporates (1) an ultra-thick cathode (>9 mAh/cm 2 ) comprising advanced cobalt-free, sodium cathode active material, (2) advanced fast-charging electrolyte (up to 12 mS/cm) developed using machine learning and automated high-throughput screening technology by UM, and (3) ceramic modified separator that enable smooth Na transport and deposition, developed at MIT, enabling a high-energy density anode-free configuration and maximizing the energy density of sodium batteries. The team has successfully combined these approaches to sodium chemistry and paved the way to meeting the fast-charging, high-energy density, and low-cost requirements of next-generation drone, electric vertical take-off and -landing, and electric vehicle batteries. Performance for anode-free sodium cells developed under this program is more powerful than the commercial Li-ion batteries. The final deliverable cell design has achieved over 300 Wh/kg and volumetric energy density above 800 Wh/L (Table 1). Additionally, the team has achieved over (1) a lifetime of 340 cycles, (2) 80% capacity retention at -20 °C (compared 25 °C), and (3) the ability to fast charge to 80% SOC in 20 minutes.

25 ENERGY STORAGE↗

DOE OSTI2025

Glancing Angle Deposition in Gas Sensing: Bridging Morphological Innovations and Sensor Performances

Glancing Angle Deposition (GLAD) has emerged as a versatile and powerful nanofabrication technique for developing next-generation gas sensors by enabling precise control over nanostructure geometry, porosity, and material composition. Through dynamic substrate tilting and rotation, GLAD facilitates the fabrication of highly porous, anisotropic nanostructures, such as aligned, tilted, zigzag, helical, and multilayered nanorods, with tunable surface area and diffusion pathways optimized for gas detection. This review provides a comprehensive synthesis of recent advances in GLAD-based gas sensor design, focusing on how structural engineering and material integration converge to enhance sensor performance. Key materials strategies include the construction of heterojunctions and core–shell architectures, controlled doping, and nanoparticle decoration using noble metals or metal oxides to amplify charge transfer, catalytic activity, and redox responsiveness. GLAD-fabricated nanostructures have been effectively deployed across multiple gas sensing modalities, including resistive, capacitive, piezoelectric, and optical platforms, where their high aspect ratios, tailored porosity, and defect-rich surfaces facilitate enhanced gas adsorption kinetics and efficient signal transduction. These devices exhibit high sensitivity and selectivity toward a range of analytes, including NO2, CO, H2S, and volatile organic compounds (VOCs), with detection limits often reaching the parts-per-billion level. Emerging innovations, such as photo-assisted sensing and integration with artificial intelligence for data analysis and pattern recognition, further extend the capabilities of GLAD-based systems for multifunctional, real-time, and adaptive sensing. Finally, current challenges and future research directions are discussed, emphasizing the promise of GLAD as a scalable platform for next-generation gas sensing technologies.

Chemistry↗

DOE OSTI2026

Strong gradient neoclassical transport in the plateau regime

Strong gradient regions in tokamaks such as the pedestal or internal transport barriers are regions of reduced turbulence where neoclassical transport can play a dominant role. In pedestals, gradient lengths comparable to the ion poloidal gyroradius have been measured. Standard neoclassical theory can miss important strong gradient effects in these regions because it assumes that the gradient length scales of density, temperature and potential are larger than the ion poloidal gyroradius. We extend plateau regime neoclassical theory into regions of gradients of the order of the ion poloidal gyroradius to capture strong gradient effects on transport processes in the pedestal and internal transport barriers. The fundamental idea behind our new framework is to keep a scale separation between the orbit widths and the gradient length scales by performing a large aspect ratio expansion. In the plateau regime, strong gradients cause poloidal variation that is in–out as well as up–down asymmetric. We study two different test cases assuming either radial force balance or the absence of turbulence and show that strong gradient effects can enhance or reduce standard neoclassical theory predictions in the plateau regime in strong gradient regions.

fusion plasma↗

DOE OSTI2026

A Chemist’s Guide to Solid-State Qubits: Diamond Color Centers as Embedded Vacancy-Centered Molecules

Diamond point defects or color centers are at the crux of solid-state quantum technology applications, from computing to sensing. Modeling molecular qubits has advanced by leaps and bounds mainly due to the advanced quantum mechanical (QM) tools in the arsenal of quantum chemists for modeling (isolated) molecular systems, which should be easily translatable for analogous systems in the solid state. Here, we simulate a series of diamond defects: a carbon vacancy (V C ) and second row substitutions that are adjacent to a V C or D C V C , with D = B, N, and O, to evaluate their magnetic and optical properties. We applied molecular orbital principles, an embedded cluster method, and a multiconfigurational QM theory to computationally characterize the above-mentioned defects for a multitude of spin, charge, and excited states. Our computational approach delivered high-quality QM insights that compare well with experiments. Furthermore, with this framework, we confirm the spin-triplets B C V C – and O C V C 0 to be analogous to N C V C – , with calculated bright transitions that respectively correspond to UV (3.80 eV) and blue (2.65 eV) light absorption and emission lifetimes (τ) of 2.3 and 5.6 ns, which are higher in energy and more short-lived (and thus brighter) than that for N C V C – : green (2.26 eV), τ = 9.5 ns.

Martirez, John Mark P. [Princeton Plasma Physics L↗

DOE OSTI2026

Field-tailoring quantum materials via magneto-synthesis: metastable metallic and magnetically suppressed phases in a trimer iridate

We demonstrate that applying modest magnetic fields (< 0.1 T) during high-temperature crystal growth can profoundly alter the structure and ground state of a spin-orbit-coupled, antiferromagnetic trimer lattice. Using BaIrO₃ as a model system, whose ground state is intricately dictated by the trimer lattice, we show that magneto-synthesis , a field-assisted synthesis approach, stabilizes a structurally compressed, metastable metallic and magnetically suppressed phases inaccessible via conventional methods. These effects include a 0.85% reduction in unit cell, 4-order-of-magnitude decrease in resistivity, a 10-fold enhancement of the Sommerfeld coefficient, and the collapse of long-range magnetic order -- all intrinsic and bulk in origin. First-principles calculations confirm that the field-stabilized structure lies substantially above the ground state in energy, highlighting its metastable character. These large, coherent and correlated changes across multiple bulk properties, unlike those caused by dilute impurities, defects or off-stoichiometry, point to an intrinsic field-induced mechanism. The findings establish magneto-synthesis as a powerful new pathway for accessing non-equilibrium quantum phases in strongly correlated materials.

magneto-synthesis↗