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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Assessing the Performance of Exchange‐Correlation Density Functionals in Describing the Iron‐Catalyzed Ammonia Synthesis System

Density functional theory (DFT) has been widely employed for elucidating mechanistic aspects of heterogeneous catalysis. However, the accuracy of DFT calculations relies heavily on selecting an exchange-correlation (XC) functional that correctly describes the electronic structure of materials involved in the reactions. This study assesses the accuracy of several XC density functionals for modeling the iron-catalyzed ammonia synthesis system. In the assessment of functional accuracy, experimental references are compared to DFT-calculated values for the formation energy of gas-phase ammonia and nitrogen, bulk Fe/Fe-nitride (γ′-Fe 4 N) lattice constants and cohesive/formation energies, and nitrogen and ammonia binding energies on Fe(100), Fe(111), Fe(110), and γ′-Fe 4 N(111). It is observed that the experimental value for each of these descriptors is accurately modeled by at least one functional. RPBE alone provides reliable estimates for both the lattice constant and cohesive energy of Fe and γ′-Fe 4 N. Temperature-programmed desorption experiments led to estimates for N and NH 3 adsorption across several Fe-based facets that are best captured by RPBE. These results highlight the importance of choosing an appropriate XC functional that accurately describes experimental systems and offer insights into effectively modeling the interactions between nitrogen and ammonia on Fe-based surfaces.

adsorption↗
DOE OSTI2020

Polyhydrido Copper Nanoclusters with a Hollow Icosahedral Core: [Cu 30 H 18 {E 2 P(OR) 2 } 12 ] (E=S or Se; R= n Pr, i Pr or i Bu)

Although atomically precise polyhydrido copper nanoclusters are of prime interest for a variety of applications, they have so far remained scarce. Here, we describe the synthesis of a dithiophosphate-protected copper(I) hydride-rich nanocluster (NC), [Cu 30 H 18 {S 2 P(O n Pr) 2 } 12 ] ( 1 H ), fully characterized by various spectroscopic methods and single-crystal X-ray diffraction. The X-ray structure of 1 H reveals an unprecedented central Cu 12 hollow icosahedron. Six faces of this icosahedron are capped by Cu 3 triangles, the whole Cu 30 core being wrapped by twelve dithiophosphate ligands and the whole cluster has ideal S 6 symmetry. The locations of the 18 hydrides in 1 H were ascertained by a single-crystal neutron diffraction study. They are composed of three types: capping μ 3 -H, interstitial μ 4 -H (seesaw) and μ 5 -H ligands (square pyramidal), in good agreement with the DFT simulations. The numbers of hydrides and ligand resonances in the 1 H NMR spectrum of 1 H are in line with their coordination environment in the solid state, retaining the S 6 symmetry in solution. Furthermore, two new Se-protected polyhydrido copper nanoclusters, [Cu 30 H 18 {Se 2 P(OR) 2 } 12 ] ( 2 H : R= i Pr 3 H : R= i Bu) were synthesized from their sulfur relative 1 H via ligand displacement reaction and their X-ray structures feature the exceptional case where both the NC shape and size are fully conserved during the course of ligand exchange. DFT and TD-DFT calculations allow understanding the bonding and optical properties of clusters 1 H – 3 H . In addition, the reaction of 1 H with [Pd(PPh 3 ) 2 Cl 2 ] in the presence of terminal alkynes led to the formation of new bimetallic Cu–Pd alloy clusters [PdCu 14 H 2 {S 2 P(O n Pr) 2 } 6 (C≡CR) 6 ] ( 4 : R=Ph; 5 : R = C 6 H 4 F).

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

Computational and Experimental Investigation of Chiral and Achiral Two‐Dimensional Organic Lead Bromide Perovskites: Octahedral Distortions and Electronic and Optical Properties

A computational investigation is presented, in conjunction with synthesis and experimental characterization, into the structural, electronic, and optical properties of layered two-dimensional organic lead bromide perovskites. Materials based on the chiral (R/S)-4-fluoro-α-methylbenzylammonium (R/S-FMBA), which have been shown to lead to bright room-temperature circularly polarized luminescence, are contrasted with the similar achiral 4-fluorobenzylammonium (FBA). Using density functional theory (DFT) with van der Waals (vdW) corrections, relaxed structures (compared with X-ray diffraction, XRD) and optical absorption spectra (compared with experiments) are studied, as well as band structure and orbital character of transitions. A Python code is developed and provided to calculate octahedral distortions and compare DFT and XRD results, finding that vdW corrections are important for accuracy and that DFT overestimates octahedral tilt angles. (FMBA) 2 PbBr 4 shows among the largest tilt angle differences (often termed Δ β ) reported, 14°–15°, indicating strong inversion symmetry-breaking, which enables its chiral emission. A large resulting Dresselhaus spin-splitting effect is found. The lowest-energy optical transitions involve the perovskite only and are polarized within the layer. This work furthers understanding of structure-property relations with applications to optoelectronics and spintronics.

UV/vis spectroscopy↗
DOE OSTI2024

Halogen Thermochemistry Assessed with Density Functional Theory: Systematic Errors, Swift Corrections and Effects on Electrochemistry

Abstract Despite its sizable errors, density functional theory (DFT) is extensively used to evaluate thermochemical properties of gases, liquids and their interfaces with solids. As numerous halogen‐containing compounds appear as reactants, products and/or electrolytes in electrochemical reactions, and ionic effects are currently an active area of research, it is important to evaluate the accuracy of DFT for halogen thermochemistry. Herein, we assess the formation energies of interhalogens, hydrogen halides, diatomic and atomic halogens and their ions using six widespread functionals at the GGA, meta‐GGA and hybrid levels. We observe that DFT errors with respect to experiments are correlated with the electronegativity of the species and there are systematic trends across functionals, such that swift corrections were devised. Specifically, the average of the mean absolute errors for the six functionals decreased from 0.19 eV before the corrections to 0.08 eV after them. Besides, the overall maximum absolute error (MAX) decreased from 0.76 to 0.44 eV and the average of the MAXs decreased from 0.51 to 0.24 eV. Finally, we illustrate the qualitative and quantitative impact of gas‐phase errors on the predictions of surface Pourbaix diagrams.

Chemistry↗
DOE OSTI2023

In-situ Synthesis of PuCl 3 and Corresponding Raman and Density Functional Theory Vibrational Modes

The experimental and calculated Raman spectrum of PuCl 3 has been reported for the first time. PuCl 3 is a primary species found in plutonium metal refinement, specifically in pyrochemical salt processes including multicycle direct oxide reduction, metal chlorination, and electrorefining. As such, Raman signatures of PuCl 3 could serve as potential forensic indicators of material process history. A novel technique for synthesizing PuCl 3 from the in situ chlorination of plutonium metal with HCl was developed to establish these signatures. Cerium metal surrogates were utilized to ensure optimization of the plutonium experiments and to minimize personnel exposure, and all experiments were carried out in a Raman reaction chamber designed for air-tight, high vacuum environments. In situ Raman spectroscopy was employed in conjunction with density functional theory (DFT) to investigate the vibrational modes of PuCl 3 . Associated mixed oxy and hydroxyl phases are also reported. The combined Raman and DFT results have eliminated inconsistencies in Raman mode assignments for the MX 3 family of metal chlorides having P6 3 /m symmetry, and IR modes derived from the DFT calculations are additionally presented. The data observed in this study are of potential interest to nuclear forensic analyses, nuclear sample aging, nuclear energy, plutonium processing, and stockpile stewardship.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electronic Structure of InAs and InSb Surfaces: Density Functional Theory and Angle-Resolved Photoemission Spectroscopy

The electronic structure of surfaces plays a key role in the properties of quantum devices. However, surfaces are also the most challenging to simulate and engineer. Here, in this work, the electronic structure of InAs(001), InAs(111), and InSb(110) surfaces is studied using a combination of density functional theory (DFT) and angle-resolved photoemission spectroscopy (ARPES). Large-scale first principles simulations are enabled by using DFT calculations with a machine-learned Hubbard U correction [npj Comput. Mater. 6, 180 (2020)]. To facilitate direct comparison with ARPES results, a “bulk unfolding” scheme is implemented by projecting the calculated band structure of a supercell surface slab model onto the bulk primitive cell. For all three surfaces, a good agreement is found between DFT calculations and ARPES. For InAs(001), the simulations clarify the effect of the surface reconstruction. Different reconstructions are found to produce distinctive surface states, which may be detected by ARPES with low photon energies. For InAs(111) and InSb(110), the simulations help elucidate the effect of oxidation. Owing to larger charge transfer from As to O than from Sb to O, oxidation of InAs(111) leads to significant band bending and produces an electron pocket, whereas oxidation of InSb(110) does not. The combined theoretical and experimental results may inform the design of quantum devices based on InAs and InSb semiconductors, for example, topological qubits utilizing the Majorana zero modes.

42 ENGINEERING↗
DOE OSTI2025

Discovery of a Ferromagnetic Nickel Chalcogenide Nanocluster Ni 3 S 3 H(PEt 3 ) 5

Atomically precise ligated nanoclusters (NC) are promising cluster-based materials with novel molecular architectures and tunable magnetic properties. Herein, the synthesis and characterization of a nickel sulfide NC Ni 3 S 3 H(PEt 3 ) 5 (PEt 3 = triethylphosphine) with distinct magnetic properties are reported. Magnetization measurements reveal its magnetic moment of 1.5 µ B in the solid phase, consistent with the existence of one unpaired electron predicted by density functional theory (DFT) calculations. Additionally, experimental measurements indicate the presence of ferromagnetic ordering within each Ni 3 S 3 H(PEt 3 ) 5 NC and strong coercivity at temperatures below 20 K. Ion mobility-mass spectrometry is employed in conjunction with DFT calculations and collision cross-section simulations to investigate the structure of the isolated Ni 3 S 3 H(PEt 3 ) 5 . Theoretical studies show that [Ni 3 S 3 H(PEt 3 ) 5 ] + has a planar Ni 3 S 3 core where three Ni atoms are arranged in a triangle with three bridging S atoms residing in the same plane. This structure is preserved in both solution and solid phases, which is confirmed by spectroscopic studies of Ni 3 S 3 H(PEt 3 ) 5 . Additionally, DFT calculations indicate that all spins at the Ni sites are aligned parallel, confirming the presence of ferromagnetic coupling. Overall, this study provides key insights into the structure and magnetic properties of Ni 3 S 3 H(PEt 3 ) 5 , which will facilitate the design of new NC-based magnetic materials.

Nickel sulfide nanocluster↗
DOE OSTI2023

High-throughput exploration of the WMoVTaNbAl refractory multi-principal-element alloys under multiple-property constraints

Development of next-generation gas turbines requires the design and fabrication of novel high-temperature structural materials capable of operating beyond 1300°C. Here, we propose a high-throughput alloy design framework under multiple-property constraints to discover new refractory multi-principal element alloys (MPEAs) for high-temperature applications. The framework treats the development of MPEAs as a composition-agnostic constraint satisfaction problem, i.e., no prescriptions are made concerning the design space before performing investigatory calculations. We target alloys in the WMoVTaNbAl chemistry space that are predicted to meet constraints on the following properties simultaneously: single-phase stability, density, solidus temperature, yield strength at 1300°C, and ductile-to-brittle-transition temperature. These properties are relevant to both applications in gas turbines and manufacturability. A set of 214 MoNbV-rich alloys meet these relevant constraints. These feasible alloys are investigated with density functional theory (DFT) to provide a fundamental electronic basis for their superior properties. Three compositionally representative alloys from the feasible design space (Mo 45 Nb 35 Ta 5 V 15 , Mo 25 Nb 50 V 20 W 5 , and Mo 30 Nb 35 Ta 5 V 25 W 5 ) are selected with a k-medoids-based design scheme for detailed DFT analysis and experimental characterization. The DFT analysis predicted a single-phase BCC at high temperatures with a high yield strength for all three MPEAs, in agreement with CALPHAD (CALculation of PHAse Diagrams) and experiments, respectively. These three alloys are benchmarked against a public database of 1546 MPEAs. Concerning the aforementioned constraints, the Mo 30 Nb 35 Ta 5 V 25 W 5 alloy outperforms these 1546 MPEAs. The present work demonstrates the ability of the proposed design methodology to identify candidate alloys for a given application under multiple property constraints in a combinatorically vast design space.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fundamental microscopic properties as predictors of large-scale quantities of interest: Validation through grain boundary energy trends

Correlations between fundamental microscopic properties computable from first principles, which we term canonical properties, and complex large-scale quantities of interest (QoIs) provide an avenue to predictive materials discovery. Here, we propose that such correlations can be efficiently discovered through simulations utilizing approximate interatomic potentials (IPs), which serve as an ensemble of “synthetic materials”. As a proof of principle we build a regression model relating canonical properties to the symmetric tilt grain boundary (GB) energy curves in face-centered cubic crystals, characterized by the scaling factor in the universal lattice matching model of Runnels et al. (2016), which we take to be our QoI. Our analysis recovers known correlations of GB energy to other properties and discovers new ones. We also demonstrate, using available density functional theory (DFT) GB energy data, that the regression model constructed from IP data is consistent with DFT results, confirming the assumption that the IPs and DFT belong to same statistical pool and thereby validating the approach. Regression models constructed in this fashion can be used to predict large-scale QoIs based on first-principles data and provide a general method for training IPs for QoIs beyond the scope of first-principles calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of applied potential on metal surfaces: Surface energy, Wulff shape and charge distribution

Here we use grand canonical density functional theory to predict the surface energies, Wulff shapes, charge distributions and catalytically active sites of different metal surfaces under electrochemical conditions. We propose a method for computing surface energies from grand canonical density functional theory (GC-DFT) calculations of periodic slab models and use it to compute the surface energies of the facets of Pt, Cu, and Ag crystals to predict their Wulff shapes under electrochemical conditions. GC-DFT predicts that, for the pure metals studied, solvation only slightly affects the Wulff shape while applied potentials considerably affect the surface energies and corresponding Wulff shapes. We used Bader charge analysis of GC-DFT computed electron densities to investigate the effect of applied potential on the distribution of electron density over the atoms of the surfaces of Pt, Cu, Ag, and the 75–25 Ag-Pt and Au-Ni alloys. This analysis shows that, under an applied potential, the electron density is unevenly distributed over the surface atoms and that the charges of atoms more exposed to solvent are more sensitive to bias. Our results show that the most sensitive atom to bias can be used to identify the most favorable adsorption site and thus, the active sites of electrochemical reactions, which is computationally less demanding than calculating the adsorption energies on all possible adsorption sites.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Thermodynamic re-modeling of the Yb-Sb system aided by first-principles calculations

Here, the thermodynamic description of the Yb-Sb binary system is developed by means of the CALculations of PHAse Diagrams (CALPHAD) method by combining experimental data in the literature and predictions from first-principles calculations based on density functional theory (DFT) in the literature and the present work. Two pseudopotentials of Yb are compared in the present DFT-based calculations with 14 and 13 f-electrons frozen in the core, i.e., 5p 6 6s 2 and 5p 6 6s 2 5 d 1 electrons as valence electrons, termed Yb_2 and Yb_3, respectively. It is shown that the phonon spectrum of the YbSb phase calculated using the Yb_3 pseudopotential does not have imaginary phonon modes and is subsequently used to predict its temperature dependent thermodynamic properties by the DFT-based quasiharmonic phonon calculations. The present thermodynamic database includes the Yb 16 Sb 11 phase in addition to five intermetallic phases that were considered in previous modeling studies, i.e., YbSb 2 , YbSb, Yb 11 Sb 10 , Yb 4 Sb 3 , and Yb 5 Sb 3 . The high temperature orthorhombic structure of the Yb 5 Sb 3 phase is not considered in the present work as it was stabilized by hydrogen. The associate solution model is used to describe the short-range ordering behavior in the liquid phase. The calculations from the present thermodynamic model show good agreement with thermochemical and phase equilibrium data from both the present work and the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A machine learning approach for increased throughput of density functional theory substitutional alloy studies

In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancement is based on a neural network (NN) approach to predict the initial position of minority and majority ions prior to DFT relaxation. The second advancement is to allow the NN to predict the total energy for every possibility minority ion position and select the most stable configuration in the absence of relaxing each trial minority configuration. A bismuth oxide materials system, (Bi x La y Yb z ) 2 MoO 6 , was used as the model system to demonstrate the developed methods and quantify the resulting computational speedup. Compared to a brute force method that requires the calculation of every permutation of minority configuration and subsequent DFT relaxation, a 1.3× speedup was realized if the NN predicted the initial configuration of ions prior to relaxation. Implementation of the second advancement allowed the NN to predict the total energy for all possible trial configurations and downselect the most stable configurations prior to relaxation, resulting in a speedup of approximately 37×. Validation was done by comparing position and energy between the NN and DFT predictions. A maximum position vector mean squared error (MSE) of 1.6 × 10 -2 and a maximum energy MSE of 2.3 × 10 -7 was predicted for the worst case configuration. This method demonstrates a significant computational speedup, which has the potential for even greater computational savings for larger compositional design spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics

First-principles statistical mechanics enables the prediction of thermodynamic and kinetic properties of materials, but is computationally expensive. Many approaches require surrogate models to calculate energies within Monte Carlo or molecular dynamics simulations. Inexpensive surrogates such as cluster expansions enable otherwise intractable calculations by interpolating data from higher accuracy methods, such as Density Functional Theory (DFT). Surrogate models introduce uncertainty into downstream calculations, in addition to any uncertainty inherent to DFT calculations. Bayesian frameworks address this by quantifying uncertainty and incorporating expert knowledge through priors. However, constructing effective priors remains challenging. This work introduces and describes practical strategies for building Bayesian cluster expansions, focusing on basis truncation, hyperparameter selection, and ground state replication. We analyze multiple basis truncation schemes, compare cross-validation to the evidence-approximation for hyperparameter optimization, and provide methods to find and enforce ground-state-preserving models through priors. Additionally, we compare the uncertainties between different approximations to DFT (LDA, PBE, SCAN) against the uncertainty introduced with the use of cluster expansion surrogate models. These approaches are demonstrated on the BCC Li x Mg 1-x and Li x Al 1-x alloys, which are both of interest for solid-state Li batteries. Our results provide guidelines for constructing and utilizing Bayesian cluster expansions, thereby improving the transparency of materials modeling. Furthermore, the approaches and insights developed in this work can be transferred to a wide range of cluster expansion surrogate models, including the atomic cluster expansion and related machine-learned interatomic potential architectures.

Alloy theory↗
DOE OSTI2023

$\mathrm{DRAGON}$: A multi-GPU orbital-free density functional theory molecular dynamics simulation package for modeling of warm dense matter

As progress in electronic structure theoretical methods is made, ab initio molecular dynamics (MD) based on orbital-free density functional theory (OF-DFT) is becoming increasingly more successful at substituting the traditional, very accurate but computationally costly Kohn–Sham (KS) approach for simulations of matter at the challenging warm dense matter (WDM) regime. However, despite the significant cost alleviation of eliminating the dependence on the KS orbitals, OF-DFT MD runs require ~10 2 to 10 3 CPU cores running for days, or even weeks, for simulations of systems comprised of 10 2 to 10 3 atoms, depending on thermodynamic conditions. Here we present DRAGON, a multi-GPU OF-DFT MD code for fast and efficient simulations of WDM. With a relatively small allocation of resources (4 to 8 GPU devices) it can provide an order of magnitude speedup for simulations containing $\mathscr{O}$(10 4 ) atoms and target systems composed of $\mathscr{O}$(10 5 ) atoms at conditions within the WDM regime, which is currently outside the capabilities of CPU codes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Materials Learning Algorithms (MALA): Scalable machine learning for electronic structure calculations in large-scale atomistic simulations

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

Density functional theory↗
DOE OSTI2022

Increasing the composition range of a novel τ 11 -Al 4 Fe 1.7 Si alloy with additions of Mn

Automotive applications need low-cost, lightweight, high-temperature alloys to increase vehicle efficiency. The Al–Fe–Si system provides an opportunity to develop such a material, as it consists of three low-cost elements that are all abundant in nature. Specifically, the τ 11 -Al 4 Fe 1.7 Si ternary intermetallic phase is a high-temperature, lightweight phase with high strength and good corrosion resistance. However, this phase exhibits a narrow compositional range of stability, resulting in undesirable microstructures forming during solidification and processing, limiting its use in potential applications. Density functional theory (DFT) calculations and a thermodynamically-driven experimental approach utilizing diffusion couples were employed to study the effect of Mn on the stability and composition range of τ 11 -Al 4 Fe 1.7 Si. The DFT calculations showed a decrease in the energy of the structure when alloying with Mn. Experimental results confirmed the predictions from the DFT calculations, indicating that alloying with Mn increases the compositional range, and thus the processability of this phase. New phase diagrams and equilibria are proposed by exploring and determining phase boundaries for the τ 11 -Al 4 Fe 1.7 Si phase with Mn.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Interplay Between Kondo and Magnetic Interactions in Pr 0.75 Gd 0.25 ScGeH

Combined experimental and density functional theory (DFT) study of Pr 0.75 Gd 0.25 ScGe and its hydride (Pr 0.75 Gd 0.25 ScGeH) reveals intricacies of composition-structure-property relationships in those distinctly layered compounds. Hydrogenation of the intermetallic parent, crystalizing in a tetragonal CeScSi-type structure, leads to an anisotropic volume expansion, that is, a(=b) lattice parameter decreases while the lattice expands along the c direction, yielding a net increase of cell volume. DFT calculations predict an antiparallel coupling of localized Gd and Pr magnetic moments in both materials at the ground state. While experiments corroborate this for the parent compound, there is no conclusive experimental proof for the hydride, where Pr moments do not order down to 3 K. DFT results also reveal that rare-earth – hydrogen interactions reduce spin-polarization of the Pr and Gd 5d and Sc 3d states at the Fermi energy, disrupt indirect exchange interactions mediated by conduction electrons, dramatically reduce the magnetic ordering temperature, and open a pseudo-gap in the majority-spin channel. Here, both experiments and theory show evidence of Kondo-like behavior in the hydride in the absence of an applied magnetic field, whereas increasing the field promotes magnetic ordering and suppresses Kondo-like behavior.

36 MATERIALS SCIENCE↗