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Deng, Hao

Publications and source records attributed to Deng, Hao.

15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A Data-Driven Method for Modeling Creep-Fatigue Stress- Strain Behavior Using Neural ODEs

In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress–strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep–fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain–stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke’s Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress–strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep–fatigue mechanical behavior, exceeding the standard Chaboche model’s accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep–fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form.

creep-fatigue↗
DOE OSTI2026

Physicochemical and Performance Characterization of Six Commercial Organic Solvent Nanofiltration Membranes

This work introduces a novel, gradient-free metamaterial design method based on Gaussian process regression to represent the density field of a unit cell. The dimension of the design space is determined by the covariance matrix dimension in the Gaussian process regression. We propose compressing this matrix using an autoencoder, enabling the decoder to generate the density field and effectively reduce the originally large design space to a lower-dimensional subspace. In this compressed space, we employ an active learning method, Bayesian Adaptive Direct Search (BADS), for efficient exploration of the design space. We demonstrate that for simple 2D designs aimed at maximizing unit cell stiffness, our method yields results comparable to those of standard topology optimization. Furthermore, we extend our approach to various mechanical problems, from linear elasticity to hyperelastic large deformation and elasto-plasticity under finite deformation, to 3D metamaterial design. This illustrates the method’s versatility and effectiveness across a range of applications.

Wu, Haoran↗
DOE OSTI2024

High Temperature Materials

Overview of high temperature metals, including component construction rules for advanced reactor designs, assessment of filler metal for Alloy 800H weldments, high-temperature crack growth rate testing, work packages and contributors for FY24, creep-fatigue damage summation evaluation, GCR metal needs, and high temperature metals from other programs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Highly efficient La/Ni co-doped strontium titanate catalyst for co-production of propylene and hydrogen from propane in protonic ceramic electrochemical cells

A highly efficient La/Ni co-doped strontium titanate (LSNT) perovskite catalyst is developed and integrated in a protonic ceramic electrochemical cell for co-production of propylene and high-purity hydrogen from commercial propane feedstock. Propane conversion and hydrogen production rate can be effectively enhanced under an applied current due to the electrochemical promotion effect and/or shifted reaction equilibrium induced by rapid separation of hydrogen product. Water vapor in the feed gas could significantly improve the catalyst stability by suppressing the coke formation. The propane conversion could reach up to 53% at 600 °C under a current density of 90 mA cm −2 . The LSNT catalyst also shows excellent tolerance for the sulfur contaminant in commercial propane gas. Finally, the excellent performance of the LSNT catalyst is attributed to the highly active and selective Ni species at the interface with the perovskite substrate, which are formed in situ via reduction-induced exsolution under reaction conditions.

interfacial active sites↗
DOE OSTI2024

Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential

Thermodynamic phase stability of three elemental boron allotropes, i.e., α-B, β-B, and γ-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP potentials trained with datasets from on-the-fly active learning achieve quantum mechanical level accuracy when employed in molecular dynamics simulations to predict wide-ranging thermodynamic, structural, and vibrational properties. The simulated phase diagram (500~1400 K and 0~16 GPa) agrees with experimental measurements. The SGP-based MD simulations also successfully predicted that the B13 defect is critical in stabilizing β-B below 700 K. At higher temperatures, the entropy becomes the dominant factor, making β-B the more stable phase over α-B. Furthermore, this Letter demonstrates that SGP potentials based on a training set consisting of defect-free-only systems could make correct predictions of defect-related phenomena in solid-state crystals, paving the path to investigate crystal phase stability and transitions.

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

Lowering the operating temperature of protonic ceramic electrochemical cells to <450 °C

Protonic ceramic electrochemical cells (PCECs) can be employed for power generation and sustainable hydrogen production. Lowering the PCEC operating temperature can facilitate its scale-up and commercialization. However, achieving high energy efficiency and long-term durability at low operating temperatures is a long-standing challenge. Here, in this work, we report a simple and scalable approach for fabricating ultrathin, chemically homogeneous, and robust proton-conducting electrolytes and demonstrate an in situ formed composite positive electrode, Ba 0.62 Sr 0.38 CoO 3–δ –Pr 1.44 Ba 0.11 Sr 0.45 Co 1.32 Fe 0.68 O 6–δ , which significantly reduces ohmic resistance, positive electrode–electrolyte contact resistance and electrode polarization resistance. The PCECs attain high power densities in fuel-cell mode (~0.75 W cm –2 at 450 °C and ~0.10 W cm –2 at 275 °C) and exceptional current densities in steam electrolysis mode (–1.28 A cm –2 at 1.4 V and 450 °C). At 600 °C, the PCECs achieve a power density of ~2 W cm –2 . Additionally, we demonstrate the direct utilization of methane and ammonia for power generation at <450 °C. Our PCECs are also stable for power generation and hydrogen production at 400 °C.

25 ENERGY STORAGE↗
DOE OSTI2023

Preliminary prediction of long-term aging and creep behavior of AM 316 SS

This report describes the development of initial mechanism models for the long term behavior of additively manufactured (AM), laser powder-bed fusion 316H stainless steel under the conditions expected in future advanced nuclear reactors. These models focus on key features of the material microstructure and response that differ from the conventionally-manufactured wrought material. Specifically, the report describes the development of models to capture the unique response of the AM material focusing on irradiation creep and swelling, the effect of internal stress, for example caused by dislocation structure, on precipitation, and the effect of the AM grain and dislocation structure on the macroscale creep and thermal aging behavior. This single mechanism models represent progress towards a complete, physics-based model for the long-term material behavior as well as elucidate key differences in the AM material behavior, when compared to the better-understood, conventionally-manufactured 316H.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A high-dimensional neural network potential for molecular dynamics simulations of condensed phase nickel and phase transitions

A high-dimensional neural network interatomic potential was developed and used in molecular dynamics simulations of condensed phase Ni and Ni systems with liquid–solid phase coexistence. The reference data set was generated by sampling the potential energy surface over a broad temperature-pressure domain using ab initio MD simulations to train a unified potential. Excellent agreement was achieved between bulk face-centered cubic nickel thermal expansion simulations and relevant experimental data. The same potential also yields accurate structures and diffusivities in the liquid state. The phase transition between liquid and solid phases was simulated using the two-phase interface method. The predicted melting point temperature is within a few kelvins of the literature value. Here, the general methodology could be applied to describe crystals with much more complex phase behaviors.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Structure, Stability, and Electronic Properties of Boron Suboxide: A Density Functional Theory Study

Boron suboxide (B 6 O) is a boron-rich compound derived from the α-rhombohedral boron lattice with extreme hardness and unusual semiconducting properties. In this work, density functional theory (DFT) was used to show that unit cell volume, mechanical strength, band gaps, and thermodynamic stabilities of B 6 O were influenced by the interstitial elements and point defects at the icosahedral sites. While the hexagonal unit cell volume (HUCV) varies with interstitial occupancy, it is the icosahedral defect that weakens the intrinsic bulk modulus of B 6 O. Using the hybrid HSE functional, we confirmed that the perfect B 6 O bulk is a p-type semiconductor with a direct band gap of 2.8 eV. Furthermore, by screening α-boron compounds systematically, we found that a simple octet rule may offer a consistent explanation for the variations in the computed electronic structures. The formation free energies calculated over a wide range of temperatures (0–2500 K) and pressures (0–80 GPa) predict that formations of interstitial defects become favorable only at higher temperatures (ca. 1800 K) in bulk B 6 O lattices. The nudged elastic band (NEB) method was employed to identify the minimum energy pathways for the diffusions of dislocated B and O atoms. The diffusion of icosahedral B atoms has an energy barrier of 0.16 eV. More complex B diffusion paths involving the reorganization of icosahedral boron atoms incur higher barriers (>1 eV). In contrast, the diffusion of interstitial O atoms is facile with a barrier of 0.4 eV. Lastly, successive O insertions into the α-B lattice were performed using DFT to generate a basic understanding of the oxidation process. Furthermore, these calculations provide fundamental atomistic insights into the growth of B 6 O crystals and control of their point defects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Histidine methyltransferase SETD3 methylates structurally diverse histidine mimics in actin

Actin histidine N τ -methylation by histidine methyltransferase SETD3 plays an important role in human biology and diseases. Here, we report integrated synthetic, biocatalytic, biostructural, and computational analyses on human SETD3-catalyzed methylation of actin peptides possessing histidine and its structurally and chemically diverse mimics. Our enzyme assays supported by biostructural analyses demonstrate that SETD3 has a broader substrate scope beyond histidine, including N-nucleophiles on the aromatic and aliphatic side chains. Quantum mechanical/molecular mechanical molecular dynamics and free-energy simulations provide insight into binding geometries and the free energy barrier for the enzymatic methyl transfer to histidine mimics, further supporting experimental data that histidine is the superior SETD3 substrate over its analogs. This work demonstrates that human SETD3 has a potential to catalyze efficient methylation of several histidine mimics, overall providing mechanistic, biocatalytic, and functional insight into actin histidine methylation by SETD3.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Activation of N 2 on Manganese Nitride-Supported Ni 3 and Fe 3 Clusters and Relevance to Ammonia Formation

Dual-site models were constructed to represent manganese nitride (Mn 4 N)-supported Ni 3 and Fe 3 clusters for NH 3 synthesis. Density functional theory calculations produced an energy barrier of approximately 0.55 eV for N–N bond activation at the interfacial nitrogen vacancy sites (N v ); also, the hydrogenation and removal of interfacial N is promoted by earth-abundant Ni and Fe metals. Steady-state microkinetic modeling revealed that the turnover frequencies of NH 3 production follow an order of Fe 3 @Mn 4 N ≈ Ni 3 @Mn 4 N > Mn 4 N > Fe >> Ni. Moreover, we present clear evidence that, before NH 3 formation, NH migrates from N v onto the metallic sites. Using N binding energy (BE N ) and the transition-state energy of N 2 activation (E TS ) as descriptors, we concluded that the beneficial effects owing to interfacial N v sites are the most pronounced when BE N is either too strong or too weak while E TS is high; otherwise, excessive N v sites may hinder catalyst performance.

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

Aryl Diazonium-Assisted Amidoximation of MXene for Boosting Water Stability and Uranyl Sequestration via Electrochemical Sorption

Despite that two-dimensional transition metal carbides and carbonitrides (MXenes) are burgeoning candidates for remediation of environmental pollutants, the construction of robust functionalized MXene nanosheets with a high affinity for target heavy metal ions and radionuclides remains a challenge. Here we report the successful placement of amidoxime chelating groups on Ti 3 C 2 T x MXene surface by diazonium salt grafting. The introduction of amidoxime functional groups significantly enhances the selectivity of Ti 3 C 2 T x nanosheets for uranyl ions and also greatly improves their stability in aqueous solution, enabling efficient, rapid, and recyclable uranium extraction from aqueous solutions containing competitive metal ions. Benefiting from the excellent conductivity of MXenes, the amidoxime functionalized Ti 3 C 2 T x nanosheets show outstanding electrochemical performance such that when loaded on carbon cloth the application of an electric field increases the uranium adsorption capacity from 294 to 626 mg/g, outperforming all organic electrochemical sorption materials reported previously. Finally, the present work provides an effective strategy to functionalize MXene nanosheets with fundamental implications for the design of MXene-based selective electrosorption electrode materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗