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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Critical role of acceptor dopants in designing highly stable and compatible proton-conducting electrolytes for reversible solid oxide cells

Proton-conducting electrolytes are receiving increasing attention due to their high ionic conductivity at intermediate temperatures, enabling the operation of solid oxide cells with high energy efficiency at low cost. However, the effect of B-site dopants on the properties of doped barium hafnate-cerate electrolyte materials, especially in single cells under operating conditions, has not been systematically studied. Here we report our findings in the development of a series of proton-conducting electrolytes with a general formula of BaHf 0.1 Ce 0.7 R 0.2 O 3–δ (BHCR172, R = Yb, Er, Y, Gd, Sm). Here, the results reveal that electrical conductivity, ionic transference number, chemical stability against steam and CO 2 , and compatibility with NiO during sintering are all closely correlated with the dopant size. In particular, the reaction with NiO is found to strongly affect the properties of the electrolytes and hence cell performance. Among all tested compositions, BaHf 0.1 Ce 0.7 Yb 0.2 O 3–δ (BHCYb172) shows excellent chemical stability and minimal reactivity towards NiO, as predicted from density functional theory (DFT)-based calculations and confirmed by experimental results. In addition, proton-conducting reversible solid oxide cells (P-ReSOCs) based on the optimized electrolyte composition, BHCYb172, demonstrate exceptional performance and stability, achieving a remarkable peak power density of 1.74 W cm –2 (O 2 as the oxidant) at 600 °C in the fuel cell mode and a high current density of 2.0 A cm –2 at 1.3 V and 600 °C in the steam electrolysis mode while maintaining excellent durability for over 1000 h.

25 ENERGY STORAGE↗
DOE OSTI2025

Processability and Material Behavior of NiTi Shape Memory Alloys Using Wire Laser-Directed Energy Deposition (WL-DED)

Utilizing additive manufacturing (AM) techniques with shape memory alloys (SMAs) like NiTi shows great promise for fabricating highly flexible and functionally superior 3D metallic structures. Compared to methods relying on powder feedstocks, wire-based additive manufacturing processes provide a viable alternative, addressing challenges such as chemical composition instability, material availability, higher feedstock costs, and limitations on part size while simplifying process development. This study presented a novel approach by thoroughly assessing the printability of Ni-rich Ni55.94Ti (Wt. %) SMA using the wire laser-directed energy deposition (WL-DED) technique, addressing the existing knowledge gap regarding the laser wire-feed metal additive manufacturing of NiTi alloys. For the first time, the impact of processing parameters—specifically laser power (400–1000 W) and transverse speed (300–900 mm/min)—on single-track fabrication using NiTi wires in the WL-DED process was examined. An optimal range of process parameters was determined to achieve high-quality prints with minimal defects, such as wire dripping, stubbing, and overfilling. Building upon these findings, we printed five distinct cubes, demonstrating the feasibility of producing nearly porosity-free specimens. Notably, this study investigated the effect of energy density on the printed part density, impurity pick-up, transformation temperature, and hardness of the manufactured NiTi cubes. The results from the cube study demonstrated that varying energy densities (46.66–70 J/mm3) significantly affected the quality of the deposits. Lower to intermediate energy densities achieved high relative densities (>99%) and favorable phase transformation temperatures. In contrast, higher energy densities led to instability in melt pool shape, increased porosity, and discrepancies in phase transformation temperatures. These findings highlighted the critical role of precise parameter control in achieving functional NiTi parts and offer valuable insights for advancing AM techniques in fabricating larger high-quality NiTi components. Additionally, our research highlighted important considerations for civil engineering applications, particularly in the development of seismic dampers for energy dissipation in structures, offering a promising solution for enhancing structural performance and energy management in critical infrastructure.

Dabbaghi, Hediyeh↗
DOE OSTI2023

Crystal structure of meglumine diatrizoate, (C 7 H 18 NO 5 )(C 11 H 8 I 3 N 2 O 4 )

The crystal structure of meglumine diatrizoate has been solved and refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Meglumine diatrizoate crystallizes in space group P2 1 (#4) with a = 10.74697(4), b = 6.49364(2), c = 18.52774(7) Å, β = 90.2263(3), V = 1292.985(5) Å 3 , and Z = 2. Two different crystal structures, which yielded essentially identical refinement residuals and positions of the non-H atoms, were obtained. The differences were in the H atom positions and the hydrogen bonding. One structure was 123.0 kJ/mol/cell lower in energy than the other and was adopted for the final description. The crystal structure consists of alternating double layers of cations and anions along the c-axis. The hydrogen bonds link the cations and anions into a three-dimensional framework. Each of the hydrogen atoms on the ammonium nitrogen of the cation acts as a donor in a strong N–H∙∙∙O hydrogen bond. One of these is to a hydroxyl group of another cation, and the other is to the carboxylate group of the anion. Each of the amide nitrogen atoms of the anion forms a strong N–H∙∙∙O intermolecular hydrogen bond, one to a carbonyl and the other to a carboxylate group. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Investigating magnetic van der Waals materials using data-driven approaches

In this work, we investigate magnetic monolayers of the form A i A ii B 4 X 8 based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi 2 Te 4 (MBT) using first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials’ properties. The dependence of materials’ properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. Finally, this study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Influence of Vegetation Canopy Architecture on Dynamics of Spatiotemporal Light Distribution and its Effect on Land-Biosphere-Atmosphere Fluxes of Energy, Carbon, and Water

The dynamic spatiotemporal distribution of light and micro climatic variables (wind speed, atmospheric CO 2 concentration, humidity, pressure, and air temperature) within the plant canopy as determined by the canopy architecture play a critical role in land-biosphere-atmosphere (LBA) photosynthetic carbon uptake, transpiratory water loss and energy partitioning. Vegetation models that include effects of sun and shade flecks, leaf temperature and energy balance partitioning between sensible and latent heat fluxes, and reduced turbulent mixing in dense vegetation canopies can accurately predict canopy performance, particularly its shaded leaves, which can constitute 80% of leaf area and contribute up to 50% of carbon update and 65% of water loss. However, current vegetation models with their bulk characterization of shaded leaves poorly represent the complex three dimensional (3D) canopy architecture are insufficient to capture the spatiotemporal dynamics of shaded leaves. Here, we propose to employ an x-ray computed tomography (XCT) imaging technique in combination with an automated, high throughput, image processing pipeline (3DArch, to be developed) to obtain an explicit 3D triangulated surface mesh of a plant canopy (Fig 1). 3DArch would provide a much needed technological jump in 3D LBA modeling where computer generated artificial 3D models suffer from lack of high resolution detailed canopy architecture parameters. The 3D canopy surface mesh output from 3DArch is coupled with an explicit 3D forward ray tracing model (fastTracer) to obtain a high resolution representation of the dynamic spatiotemporal light environment within the canopy over the course of a day (Fig 1). The results from the light attenuation model are then coupled with a process based multi-scale bio-physical canopy and leaf model of photosynthesis (PlantGro) to get fluxes of carbon, water and energy at the leaf and canopy scale, in addition to estimating whole plant carbon uptake, and water loss. Our proposed model framework would be the first of its kind to provide a breakthrough capability in the field of process-based plant modeling that bridges the key technological gap in shaded leaf modeling through explicit representation of 3D canopy architecture. Computer generated artificial 3D model canopy simulations on grasses show that the canopy light distribution does not follow an exponential behavior as assumed in most 1D vegetation models, but rather exhibits a heterogeneous behavior, with a small but significant portion of interior canopy leaves experiencing peak light levels due to sun flecks while the rest of the canopy receives substantially lower light levels. These results indicate a bias in traditional models that overestimates shaded leaf contribution towards whole canopy photosynthesis by up to 15% and underestimate transpiration by up to 12% with an associated increase in sensible heat fluxes since photosynthesis, transpiration and energy partitioning are tightly coupled processes. However, these test results need to be validated using the 3D architecture obtained from a real plant imaged in 3D using 3DArch, rather than an artificial model plant. Our proposed model framework would allow us to identify inefficiencies in canopy light distribution and prescribe optimal canopy architectures (leaf optical properties, leaf area, leaf angles, row spacing, planting density, etc.) to traditional and genetic engineering based crop breeders, for maximizing target functions such as light use efficiency, water use efficiency, drought tolerance, crop yield, crop resilience etc., with important applications in biofuel crops such as sorghum, and switchgrass. Our model can also be applied for designing optimal canopy architecture that increase surface albedo as a mitigation strategy for global warming and projections of crop yield for future food and water security under climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Achieving performance portability in Gaussian basis set density functional theory on accelerator based architectures in NWChemEx

The numerical integration of the exchange–correlation (XC) potential is one of the primary computational bottlenecks in Gaussian basis set Kohn–Sham density functional theory (KS-DFT). To achieve optimal performance and accuracy, care must be taken in this numerical integration to preserve local sparsity as to allow for near linear weak scaling with system size. This leads to an integration scheme with several performance critical kernels which must be hand optimized for each architecture of interest. As the set of available accelerator hardware goes more diverse, a key challenge for developers of KS-DFT software is to maintain performance portability across a wide range of computational architectures. In this article, we examine a modular software design pattern which decouples the implementation details of performance critical kernels from the expression of high-level algorithmic workflows in a device-agnostic language such as C++; thus allowing for developers to target existing and emerging accelerator hardware within a single code base. We consider the efficacy of such a design pattern in the numerical integration of the XC potential by demonstrating its ability to achieve performance portability across a set of accelerator architectures which are representative of those on current and future U.S. Department of Energy Leadership Computing Facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Facilitating Hydrogen Dissociation over Dilute Nanoporous Ti–Cu Catalysts

The dissociation of H 2 is an essential elementary step in many industrial chemical transformations, typically requiring precious metals. Here, we report a hierarchical nanoporous Cu catalyst doped with small amounts of Ti (npTiCu) that increases the rate of H 2 –D 2 exchange by approximately one order of magnitude compared to the undoped nanoporous Cu (npCu) catalyst. The promotional effect of Ti was measured via steady-state H 2 –D 2 exchange reaction experiments under atmospheric pressure flow conditions in the temperature range of 300–573 K. Pretreatment with flowing H 2 is required for stable catalytic performance, and two temperatures, 523 and 673 K, were investigated. The experimentally determined H 2 –D 2 exchange rate is 5–7 times greater for npTiCu vs the undoped Cu material under optimized pretreatment and reaction temperatures. The H 2 pretreatment leads to full reduction of Cu oxide and partial reduction of surface Ti oxide species present in the as-prepared catalyst as demonstrated using in situ ambient pressure X-ray photoelectron spectroscopy and X-ray absorption spectroscopy. The apparent activation energies and pre-exponential factors measured for H 2 –D 2 exchange are substantially different for Ti-doped vs undoped npCu catalysts. Density functional theory calculations suggest that isolated, metallic Ti atoms on the surface of the Cu host can act as the active surface sites for hydrogen recombination. Furthermore, the increase in the rate of exchange above that of pure Cu is caused primarily by a shift in the rate-determining step from dissociative adsorption on Cu to H/D atom recombination on Ti-doped Cu, with the corresponding decrease in activation entropy that it produces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A combined theoretical and experimental study of small anthracene–water clusters

Water-cluster interactions with polycyclic aromatic hydrocarbons (PAHs) are of paramount interest in many chemical and biological processes. Here we report a study of anthracene monomers and dimers with water (up to four)-cluster systems utilizing molecular beam vacuum-UV photoionization mass spectrometry and density functional calculations. Structural loss in photoionization efficiency curves when adding water indicates that various isomers are generated, while theory indicates only a slight shift in energy in photoionization states of different isomers. Calculations reveal that the energetic tendency of water is to remain clustered and not to disperse around the PAH. Theoretically, we observe water confinement exclusively in the case of four water clusters and only when the anthracenes are in a cross configuration due to optimal OH∙∙∙π interactions, indicating dependence on the size and structure of the PAH. Furthermore theory sheds light on the structural changes that occur in water upon ionization of anthracene, due to the optimal interactions of the resulting hole and water hydrogen atoms.

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

A charge-density-based general cation insertion algorithm for generating new Li-ion cathode materials

Future lithium (Li) energy storage technologies, in particular solid-state configurations with a Li metal anode, opens up the possibility of using cathode materials that do not necessarily contain Li in its as-made state. To accelerate the discovery and design of such materials, we develop a general, chemically, and structurally agnostic methodology for identifying the optimal Li sites in any crystalline material. For a given crystal structure, we attempt multiple Li insertions at symmetrically in-equivalent positions by analyzing the electronic charge density obtained from first-principles density functional theory. In this report, we demonstrate the effectiveness of this procedure in successfully identifying the positions of the Li ion in well-known cathode materials using only the empty host (charged) material as guidance. Furthermore, applying the algorithm to over 2000 candidate cathode empty host materials we obtain statistics of Li site preferences to guide future developments of novel Li-ion cathode materials, particularly for solid-state applications.

25 ENERGY STORAGE↗
DOE OSTI2025

Optimization of a Secondary Air Injector for a Rich-Quench-Lean (RQL) Ammonia Combustor Using Computational Fluid Dynamics

Ammonia has emerged as a promising medium for moving hydrogen around the globe due to its energy density, existing infrastructure for production, transportation, and storage, and multiple applications from fertilizer to power generation. However, one of the most significant challenges with ammonia combustion for power generation is the formation of nitrogen oxides (NOx) during combustion. Several combustion strategies have been developed to minimize the formation of NOx. One such strategy, known as rich-quench-lean (RQL), is a method that combusts NH3 in a fuel-rich environment, followed by a quick mix section and a lean burnout section. Rapid mixing of secondary air before lean burnout is thought to be important to minimize the formation of NOx. A genetic algorithm (GA) is used to parametrically vary the secondary air injector design, including the diameter, count, and angles over a constrained design space. The designs are evaluated using a non-reacting OpenFOAM model, with the objective function being the uniformity index of the secondary air (modeled as a scalar). Optimization campaigns show that traditional correlation-based designs might not be adequate. After running 414 OpenFOAM models, the optimal design is 32, 1 mm diameter, angled air injectors, increasing the uniformity index at the outlet of the quick mix zone by 36.8%. The most promising designs will be manufactured and tested in a small RQL burner setup, which is expected to lead to validation and insight into the practical usage of NH3 combustion for power generation.

ammonia combustion↗
DOE OSTI2024

Crystal structure of anthraquinone-2-carboxylic acid, C 15 H 8 O 4

The crystal structure of anthraquinone-2-carboxylic acid has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Anthraquinone-2-carboxylic acid crystallizes in space groupP-1 (#2) witha= 3.7942(2),b= 13.266(5),c= 22.835(15) Å,α= 73.355(30),β= 89.486(6),γ= 86.061(1)°,V= 1098.50(7) Å 3 , andZ= 4. The crystal structure contains two independent molecules of anthraquinone-2-carboxylic acid. Although the expected hydrogen-bonded dimers are present, the dimers are not centrosymmetric. The dimer contains one molecule of each planar low-energy conformation. The crystal structure consists of a herringbone array of centrosymmetric pairs of molecules parallel to thebc-plane. The molecules stack along the shorta-axis. The powder pattern has been submitted to ICDD® for inclusion in the Powder Diffraction File™ (PDF®).

Materials Science↗
DOE OSTI2026

Neural network potentials with effective charge separation for non-equilibrium dynamics of ionic solids: a ZnO case study

Developing neural network potentials (NNPs) accurate under non-equilibrium dynamics is challenging, as such systems require extensive sampling beyond equilibrium phases. Here we construct high-fidelity NNPs for zinc oxide (ZnO), a polymorphic ionic solid, using density functional theory (DFT) reference data. To efficiently capture transitional configurations, we combine enhanced-sampling molecular dynamics with empirical potentials, data distillation, and pretraining on short-range atomic energies (A-Train), followed by transfer learning with DFT-relabeled datasets. This hierarchical approach improves transferability across polymorphs and stress states. We further introduce effective charge separation, treating long-range Coulombic terms analytically while short-range residual interactions are learned by the NNP. The optimal effective charges fall in the range 0.5–1.0 q e , consistent with dielectric-screened values derived from formal charges but distinct from Bader estimates. Motivated by this observation, we propose a simple data-driven protocol in which effective charges are optimized by comparing DFT reference energies with explicit Coulomb calculations, without additional NNP training. This strategy improves accuracy and transferability in DFT-level predictions of energies, forces, and stress. Together, these results provide a practical charge-selection framework for robust NNP development in ionic solids, enabling reliable simulation of polymorphic phase transformations and non-equilibrium dynamics.

Chemistry↗
DOE OSTI2024

Crystal structure of ractopamine hydrochloride, C 18 H 24 NO 3 Cl

The crystal structure of ractopamine hydrochloride has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Ractopamine hydrochloride crystallizes in space groupPbca(#61) witha= 38.5871(49),b= 10.7691(3),c= 8.4003(2) Å,V= 3490.75(41) Å 3 , andZ= 8. The ractopamine cation contains two chiral centers, and the sample consists of a mixture of the S,S/R,R/S,R and R,S forms. Models for the two diastereomers S,S and S,R were refined, and yielded equivalent residuals, but the S,R form is significantly lower in energy. The crystal structure consists of layers of molecules parallel to thebc-plane. In each structure one of the H atoms on the protonated N atom acts as a donor in a strong discrete N–H⋯Cl hydrogen bond. Hydroxyl groups act as donors in O–H⋯Cl and O–H⋯O hydrogen bonds. Both the classical and C–H⋯Cl and C–H⋯O hydrogen bonds differ between the forms, helping to explain the large microstrain observed for the sample. The powder pattern has been submitted to ICDD® for inclusion in the Powder Diffraction File™ (PDF®).

Materials Science↗
DOE OSTI2024

Influence of Alkali Metal Cations on the Oxygen Reduction Activity of Pt 5 Y and Pt 5 Gd Alloys

Electrolyte species can significantly influence the electrocatalytic performance. In this work, we investigate the impact of alkali metal cations on the oxygen reduction reaction (ORR) on active Pt 5 Gd and Pt 5 Y polycrystalline electrodes. Due to the strain effects, Pt alloys exhibit a higher kinetic current density of ORR than pure Pt electrodes in acidic media. In alkaline solutions, the kinetic current density of ORR for Pt alloys decreases linearly with the decreasing hydration energy in the order of Li + > Na + > K + > Rb + > Cs + , whereas Pt shows the opposite trend. To gain further insights into these experimental results, we conduct complementary density functional theory calculations considering the effects of both electrode surface strain and electrolyte chemistry. The computational results reveal that the different trends in the ORR activity in alkaline media can be explained by the change in the adsorption energy of reaction intermediates with applied surface strain in the presence of alkali metal cations. Our findings provide important insights into the effects of the electrolyte and the strain conditions on the electrocatalytic performance and thus offer valuable guidelines for optimizing Pt-based electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Comparing measurement of limiting current in block copolymer electrolytes as a function of salt concentration with theoretical predictions

Optimizing electrolyte performance is crucial for the widespread adoption of electrochemical energy storage. We demonstrate that limiting current provides a robust criterion for determining the optimum electrolyte. Experiments were conducted on rigid block copolymer electrolytes comprising mixtures of polystyrene-block-poly(ethylene oxide) copolymer (SEO) and lithium bis(trifluoromethanesulfonyl) imide salt (LiTFSI) over a salt concentration range from r av = 0.04 to r av = 0.20 (r av is the molar ratio of lithium ions to ethylene oxide). Here we show that the maximum limiting current density is 4.3 mA cm –2 at r av = 0.12. The dependence of limiting current on salt concentration is in good agreement with predictions from Newman's concentrated solution theory with no adjustable parameters.

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

Exciton Transfer Between Extended Electronic States in Conjugated Inter-Polyelectrolyte Complexes

Artificial light harvesting, a process that involves converting sunlight into chemical potential energy, is considered to be a promising part of the overall solution to address urgent global energy challenges. Conjugated polyelectrolyte complexes (CPECs) are particularly attractive for this purpose due to their extended electronic states, tunable assembly thermodynamics, and sensitivity to their local environment. Importantly, ionically assembled complexes of conjugated polyelectrolytes can act as efficient donor-acceptor pairs for electronic energy transfer (EET). However, to be of use in material applications, we must understand how modifying the chemical structure of the CPE backbone alters the EET rate beyond spectral overlap considerations. In this report we investigate the dependence of the EET efficiency and rate on the electronic structure and excitonic wave function of the CPE backbone. To do so, we synthesized a series of alternating copolymers where the electronic states are systematically altered by introducing comonomers with electron withdrawing and electron-rich character while keeping the linear ionic charge density nearly fixed. We find evidence that the excitonic coupling may be significantly affected by the exciton delocalization radius, in accordance with analytical models based on the line-dipole approximation and quantum chemistry calculations. Our results imply that care should be taken when selecting CPE components for optimal CPEC EET. These results have implications for using CPECs as key components in water-based light-harvesting materials, either as standalone assemblies or as adsorbates on nanoparticles and thin films.

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

Towards defect engineering in hexagonal MoS 2 nanosheets for tuning hydrogen evolution and nitrogen reduction reactions

Combined computational and experimental approaches were used to evaluate defective 2H-MoS 2 nanosheets for their activity and selectivity for hydrogen evolution reaction (HER) and nitrogen reduction reaction (NRR). Density functional theory calculations were used to understand the relationship between HER and NRR activity on the ideal basal MoS 2 plane, seven grain-boundaries, ten single-/few-atom vacancies and anti-sites, and zigzag and armchair edge sites. The results confirm that 2H-MoS 2 should contain several defects with high activity for HER: armchair and zigzag edges, VS vacancy, MoS 2 anti-site, and S-S and Mo-Mo grain boundaries. Considering Gibbs free energy change for all the steps in the NRR mechanism and kinetic barriers for a key NRR step, we have found that activation of perspective NRR selective sites in 2H-MoS 2 , namely V MoS6 and clusters of S-vacancies, would require large overpotential, conditions at which HER dominates. The DFT conclusions are supported by the electrochemical studies of NRR activity and selectivity under aqueous conditions, which show an increase in NRR activity but a decrease in Faradaic efficiency as applied cell potential becomes more negative. The results of this work therefore highlight the challenges in activating natural 2H-MoS 2 for NRR, which would require additional material engineering or reaction condition optimization as a way to suppress HER, decrease the NRR overpotential or preferentially both.

2H-MoS2↗
DOE OSTI2021

Actinide Molten Salts: A Machine-Learning Potential Molecular Dynamics Study

We know that actinide molten salts represent a class of important materials in nuclear energy. Understanding them at a molecular level is critical to proper and optimal design of relevant technological applications. Yet, owing to the complexity of electronic structure due to the 5f orbitals, computational studies of heavy elements in condensed phases using ab initio potentials to study the structure and dynamics of these elements embedded in molten salts are difficult. This lack of efficient computational protocols makes it difficult to obtain information on properties that require extensive statistical sampling like transport. To tackle this problem, we adopted a machine-learning approach to study ThCl 4 -NaCl and UCl 3 -NaCl binary systems. The machine-learning potential, with the density functional theory accuracy, allows us to obtain long molecular dynamics trajectories (ns) for large systems (10 3 atoms) at a considerably low computing cost, thereby efficiently gaining information about their bonding structures, thermodynamics, and dynamics at a range of temperature. We observed a considerable change in the coordination environments of actinide elements and their characteristic coordination-sphere lifetime. Our study also suggests that actinides in molten salts may not follow well known entropy-scaling laws.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗