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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Cooperative Effects Associated with High Electrolyte Concentrations in Driving the Conversion of CO2 to C2H4 on Copper

Compared to a conventional electrolyte concentration of 1 M HCOOK, the use of a highly concentrated 7.1 M HCOOK electrolyte increases the Faradaic efficiency (FE) ratio of C2H4/CO from 2.2 +- 0.3 to 18.3 +- 4.8 at -1.08 V vs. reversible hydrogen electrode (RHE) on a Cu gas-diffusion electrode. Based on electrochemical analysis and ab initio molecular dynamics (AIMD) simulation, the identity and concentration of the cation and anion play more important roles in controlling the CO2R reaction pathway than the bulk CO2 solubility and the bulk pH of electrolytes. In situ attenuated reflectance surface enhanced infrared absorption spectroscopy (ATR-SEIRAS) suggests that, unlike 1 M HCOOK, the *CO-bridge-binding mode on Cu is dominant in 7.1 M HCOOK electrolyte, which potentially results in less CO release and higher yield of C2H4. This study demonstrates that although we can tailor the electrolyte composition to shift product selectivity, the factors that control the product selectivity are numerous and cannot be distilled down into one correlated property-reactivity relationship.

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

Evaluation of Amorphous Oxide Coatings for High-Voltage Li-Ion Battery Applications Using a First-Principles Framework

Cathode surface coatings are widely used industrially as a means to suppress degradation and improve electrochemical performance of lithium-ion batteries. However, developing an optimal coating is challenging, as different coating materials may enhance one aspect of performance while hindering another. Furthermore, to elucidate the fundamental thermodynamic and transport properties of amorphous cathode coating materials, here, we present a framework for calculating and analyzing the Li + and O 2- transport and the stability against delithiation in such materials. Our framework includes systematic workflows of ab-initio molecular dynamics calculations to obtain amorphous structures and diffusion trajectories coupled with an analysis of critical changes of the active-ion local environment during diffusion. Based on these data, we provide an estimate of room-temperature diffusivities, including statistical error bars, and the evaluation of the coating suitability in terms of its ability to facilitate Li + transport while blocking O 2- transport. Finally, we add the thermodynamic stability analysis of the coating chemistry within the operating voltage of common Li-ion cathodes. We apply this framework to two commonly used amorphous coating materials, Al 2 O 3 and ZnO. We find that (1) in general, a higher Li + content increases both Li + and O 2- diffusivities in both Al 2 O 3 and ZnO. Also, Li + and O 2- diffuse much faster in ZnO than in Al 2 O 3 . (2) However, neither Al 2 O 3 nor ZnO is expected to retain a significant concentration of Li + at high charge. (3) ZnO performs much more poorly in terms of O 2- blocking, and hence, Al 2 O 3 is preferred for high-voltage cathode applications. These results will help to quantitatively evaluate amorphous materials, such as metal oxides and fluorides, for different performance metrics and facilitate the development of optimal cathode coatings.

25 ENERGY STORAGE↗
DOE OSTI2025

Elucidating the Role of Hydrogen at c‐In 2 O 3 /a‐In 2 O 3− x Interface

Ab initio molecular dynamics simulations and hybrid functional electronic structure calculations are employed to determine the formation, the structural and electronic properties, and the dynamics of covalent (In—OH) and ionic (In—H—In) hydrogen defects at crystalline-In 2 O 3 /amorphous-In 2 O 3−x interface. This comprehensive computational study considers (i) various interstitial and substitutional hydrogen site locations within the crystalline, amorphous, and interfacial regions; (ii) several oxygen-to-hydrogen ratios; and (iii) possible defect charged states. The results reveal hydrogen's inability to fully passivate the undercoordinated under-shared in atoms in amorphous highly substoichiometric oxide, giving rise to the formation of deep electron traps even in net-charge neutral structures. These trap defects are found to be sensitive to photoexcitation, in contrast to In—OH with H's electronic states located below the valence band and to In—H—In, where H is found to maintain its charge state upon illumination. Nevertheless, H plays a critical role in photoinduced conductivity and its relaxation by promoting In—O coordination transformations at the interfacial region, including deterioration of the crystalline layer. Finally, the results help identify mobile H species and metastable H defect complexes (such as In—H—H—In, In—OH–H—In, and In—OH–O—In) that are responsible for long relaxation times of the conductivity decay.

H defects↗
DOE OSTI2021

Confinement effects on the solvation structure of solvated alkaline metal cations in a single-digit 1T-MoS 2 nanochannel: A first-principles study

Confinement plays an important role in determining ion transport in porous materials, which, in turn, may influence the performance of many energy storage and desalination devices. In this work, we combined density functional theory (DFT) with an implicit solvation model and ab initio molecular dynamics (AIMD) to investigate the effects of nanoconfinement on several solvated alkaline metal cations in a single-digit 1T-MoS 2 nanochannel. Our DFT calculations with a solvation model indicated that cations with stronger hydration energy introduce a higher number of co-intercalated water molecules into the channel, consistent with early experimental observation obtained for MXene (2D transition metal carbide) channels. The predicted optimal water numbers for the cations were then used for AIMD simulations that explicitly include the effects of the solvent. When compared with the cations in bulk solution, our simulations showed that the hydration structure and coordination number (CN) of the solvated cations confined in the MoS 2 channel can be significantly altered. Further, we found that larger cations with weaker hydration energy (K + , Rb + , and Cs + ) exhibited a distinctive CN decrease under confinement, while smaller cations (Li + and Na + ) retained a similar hydration shell as in the bulk solution. More specifically, the hydration shell of large cations (K + , Rb + , and Cs + ) in MoS 2 showed similar features of the coordination angle to the bulk, which suggests the partially broken hydration shell with no geometry change under confinement. Our simulations provided insights into the change of the hydration structure of alkaline metal cations under confinement, which may have important implications on their transport in the 1T-MoS 2 channel.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Understanding Solid-Electrolyte Interphase Formation at the Lithium Metal Anode of Lithium-Sulfur Batteries

Lithium-sulfur (Li-S) batteries are one of the most promising chemistries for the next generation of energy storage devices. In a standard Li-S cell, the metallic lithium anode is an essential component of the battery due to its low density, extremely high theoretical specific capacity, and very low negative electrochemical potential. However, several challenges related to the use of Li metal have prevented this battery technology from becoming commercially available. For instance, Li-anodes are highly reactive, which results in the continuous decomposition of the electrolyte and the formation of the solid-electrolyte interphase (SEI) layer. This, combined with the non-uniform deposition of Liions during plating at the anode surface, can seriously affect the performance, cycling, and safety of the battery. In addition, it has been suggested that a controlled SEI formation at the metallic Li anode can yield enhanced battery performance. Hence, a comprehensive understanding of how the SEI layer is formed can help elucidate improvements in this battery technology. In this work, the formation of the SEI layer in Li-S batteries is investigated using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. The stability of liquid organic-based electrolyte components, salt concentration, electron-rich environments, and the use of inorganic solid-state electrolyte (SSE) materials are explored in order to provide molecular-level fundamental insights into how the nature and composition of the electrolyte can alter the initial stages of the SEI formation. The products of electrolyte decomposition and electro-/chemical reaction pathways of liquid electrolyte component such as solvent and salts are predicted from AIMD simulations of electrolyte-anode interfaces. Energetics of reactions from DFT ground-state optimizations are also presented to confirm the decomposition mechanisms. Some significant differences are then drawn regarding the use of low and high salt concentrations. Simulations under electron-rich environments show additional multielectron electrochemical reactions of solvent and salt decomposition taking place due to the excess of electrons and the presence of radical anions in the solution. Finally, the initial stages of formation, stability, and main constituents of interfaces between Li-metal and Sbased electrodes with sulfide-based SSEs are characterized in detail.

25 ENERGY STORAGE↗
DOE OSTI2025

Trans-Influence in Dinuclear Pt(III) Complexes: Electronic Structure, σ-Donation, and Pt–Pt Spin–Spin Coupling

This study investigates the trans influence in dinuclear platinum(III) complexes using a combined approach of ab initio molecular dynamics and natural localized molecular orbital (NLMO) analysis. Focusing on pivalamidate-bridged Pt III complexes with axial ligands of varying σ-donation strength, it is quantified how ligand−metal interactions propagate through the Pt−Pt bond, and how they affect bond polarization, axial water coordination, and 1 J PtPt spin−spin coupling constants. NLMO analysis reveals quantitatively that strong σ-donating ligands polarize the Pt−Pt bond, shifting the electron density toward the opposite platinum center. The polarization mechanism is identified as the primary reason for the observed reduction of 1 J PtPt , because the bond polarization diminishes the transmission of the nuclear magnetic spin-induced electron spin density through the Pt−Pt bond. Additionally, the destabilization of axial water coordination at the opposite Pt site can be rationalized through a polarizationinduced Pt IV − Pt II -like mixed-valence character.

Ab initio molecular dynamics↗
DOE OSTI2026

pH-Driven Restructuring of Hydration Layers and Cation Ad-sorption at the Alumina-Water Interface

Oxide-water interfaces underpin ion separation, catalysis, and electrochemical energy technologies, where the electrical double layer (EDL) controls adsorption, transport, and reactivity. Yet, the molecular-scale link between pH-dependent surface protonation, hydration-layer structure, and counter-ion adsorption remains poorly defined. Here, we combine in situ crystal truncation rod (CTR) and resonant anomalous X-ray reflectivity (RAXR) with streaming potential measurements and ab initio molecular dynamics (AIMD) simulations to resolve the chemical and structural evolution of the EDL at the single-crystal alumina (012)-water interface in 10 mM Rb+ over pH 3-12. CTR measurements reveal two distinct adsorbed water layers at ~2.2 and ~3.5 Å above the surface that each shift toward the substrate at transition pHs near 6.5 and 10.6, respectively, directly reflecting changes in primary hydration layer structure in response to the deprotonation of bridging and terminal aluminol groups. RAXR shows a 10-fold increase in Rb+ coverage and a decrease in mean adsorption height from ~3.5 to ~2.7 Å with increasing pH, indicating enhanced counter-ion binding accompanied by Stern layer contraction. Streaming potential measurements demonstrate that the zeta potential, i.e., potential at the hydrodynamic shear plane, is positive at pH 3 and becomes negative at pH ≥3.5. This negative charge magnitude increases with increasing pH, consistent with progressive surface deprotonation at higher pH. AIMD identifies inner- and outer-sphere Rb+ complexes whose adsorption heights and coordination geometries depend sensitively on the protonation state of surface oxygens, providing atomistic support for the experimentally inferred trends. These measurements establish two discrete, site-specific pH transitions in hydration-layer structure that track aluminol (de)protonation and quantitatively link them to a pH-driven contraction of the Stern layer (increasing Rb+ coverage and decreasing adsorption height). This provides a direct structural basis for connecting surface acid-base chemistry to ion binding distances at an oxide-water interface.

Electrical double layer (EDL), Surface protonation↗
DOE OSTI2022

High temperature stability and transport characteristics of hydrogen in alumina via multiscale computation

Here, the impact of hydrogen charge states on the stability and transport characteristics of hydrogen interstitials in alumina polymorphs is evaluated by multiscale computational methods including density functional theory (DFT), ab initio molecular dynamics (AIMD) and machine learned force fields. Thermodynamic calculations show that the protonic H i +1 interstitial is the most stable defect species for most values of the electronic bandgap in both and amorphous alumina (Al 2 O 3 ). Further, active learned Gaussian approximation potentials (GAP) were developed using AIMD data to study temperature dependent long time proton diffusion in alumina. Diffusivity calculations from GAP-MD simulations are found to be comparable with of the AIMD data, while being ~340 times faster and scalable to larger systems. Comparisons with diffusivity values for other interstitial charge states (H i 0 and H i -1 ) and published experimental literature indicate that H i +1 diffusion is the likely mechanism of hydrogen transport. A good agreement is obtained between H i +1 diffusivity calculated in α-Al 2 O 3 from DFT: 5.05 10 -3 exp(-0.81 eV/k B /T) cm 2 /s and reported experiment: 9.7X10 -4 exp(-0.83 eV/k B /T) cm 2 /s. Computationally and experimentally calculated energy barriers (0.81 and 0.83 eV respectively) only differ by 2.5%. Similarly, the pre-exponential diffusion coefficients only differ by 0.5 orders of magnitude. Moreover, the diffusivity of H i +1 in amorphous Al 2 O 3 in the 1000–2000 K range is calculated to be 2.53X10 -2 exp(-0.89 eV/k B /T), just one order of magnitude higher than the corresponding value in α-Al 2 O 3 . This suggests that local structural disorder does not significantly affect the energy landscape and diffusion behavior of H i +1 in Al 2 O 3 . Overall, these results show promise for the application of alumina polymorphs as hydrogen permeation barriers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Effect of Framework Composition and NH 3 on the Diffusion of Cu + in Cu-CHA Catalysts Predicted by Machine-Learning Accelerated Molecular Dynamics

Cu-exchanged zeolites rely on mobile solvated Cu + cations for their catalytic activity, but the role of the framework composition in transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length and time scales or diverse compositions. We report a machine-learning interatomic potential that accurately reproduces ab initio results and effectively generalizes to allow multinanosecond simulations of large supercells and diverse chemical compositions. Biased and unbiased simulations of [Cu(NH 3 ) 2 ] + mobility show that aluminum pairing in eight-membered rings accelerates local hopping and demonstrate that increased NH 3 concentration enhances long-range diffusion. The probability of finding two [Cu(NH 3 ) 2 ] + complexes in the same cage, which is key for SCR-NOx reaction, increases with Cu content and Al content but does not correlate with the long-range mobility of Cu + . Supporting experimental evidence was obtained from reactivity tests of Cu-CHA catalysts with a controlled chemical composition.

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

Short- to Intermediate-Range Structure, Transport, and Thermophysical Properties of LiF–NaF–ZrF4 Molten Salts

LiF–NaF–ZrF 4 multicomponent molten salts are identified as promising candidates for coolant salts in molten salt reactors and advanced high-temperature reactors. This study focused on low-melting point salt compositions of interest: 38LiF–51NaF–11ZrF 4 , 42LiF–29NaF–29ZrF 4 , and 26LiF–37NaF–37ZrF 4 . Ab-initio molecular dynamics (AIMD) calculations were performed and compared with available experimental data to assess the ability of rigid ion models (RIM) to reproduce short- to intermediate-range structure, transport, and thermophysical properties of the LiF–NaF–ZrF 4 salt mixtures. It is found that as ZrF 4 mol% increases, the average cation–anion coordination number (CN) of monovalent cations (Li + , Na + ) obtained from RIM calculations decreases, while multivalent Zr 4+ CN varied from 15% to 19% in comparison to corresponding AIMD values. In addition, RIM is found to predict the existence of 7, 8, and 9 coordinated fluorozirconate complexes, while AIMD and the available experimental data showed an occurrence of 6, 7, and 8 coordinated complexes in the melt. The intermediate-range structure analysis revealed that while the RIM parameters are able to reproduce a local structure for lower ZrF 4 mol% salts such as in 38LiF–51NaF–11ZrF 4 , an extensive fluorozirconate network formation is observed in RIM simulations for higher ZrF 4 mol% compositions. The network generated by RIM parameters is found to be mainly connected by “corner-sharing” fluorozirconate complexes as opposed to both “edge-sharing” and “corner-sharing” connectively portrayed by AIMD. It is found that a close agreement between AIMD and the RIM salt structure for the 11-mol% ZrF 4 salt resulted in good agreement in the calculated Zr diffusivities and the viscosity values. However, due to the inaccurate short- to intermediate-range structure prediction by RIM for higher ZrF 4 mol% compositions, thermophysical properties such as densities and heat capacity differ by up to 26% and 27%, respectively, upon comparison with AIMD and experimental values. Also, the network-dominated properties such as diffusion coefficients and viscosities differed by up to two and three orders of magnitude, respectively. This study signifies the importance of accurate salt structure generation for an accurate prediction of transport and thermophysical properties of multicomponent molten salts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Experimental and theoretical investigation into the high pressure deflagration products of 2,6-diamino-3,5-dinitropyrazine-1-oxide (LLM-105)

Diamond anvil cell (DAC) laser ignition experiments and reactive ab initio molecular dynamics (AIMD) simulations were performed on the high explosive (HE) LLM-105 to investigate its high pressure (HP) deflagration chemistry. Raman and optical spectroscopy measurements reveal LLM-105 reacts into an opaque carbonaceous product at 4–25 GPa. At pressures >~ 27 GPa, the reaction product consists of an amorphous optically transparent solid and nitrogen (N 2 ) in the solid phase. While not a one-to-one comparison due to the small time and length scales, the HP AIMD simulations show that some of the product is molecular N 2 , in qualitative agreement with experiment, while above 20 GPa most of the product consists of large amorphous C x H y N z O k clusters. Clustering is enhanced with pressure and reduces with temperature. In the experiments with initial sample pressure >~ 25 GPa, the pressure within the DAC decreases with minimal change in DAC cavity area. At initial sample pressures of 43.9 GPa, when quenched to 0 K, simulations predict a product experiencing a lower pressure consistent with the experimental measurement at lower load pressures. In conclusion, the results are important for understanding the HP deflagration chemistry of LLM-105.

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

An isolated water droplet in the aqueous solution of a supramolecular tetrahedral cage

Water under nanoconfinement at ambient conditions has exhibited low-dimensional ice formation and liquid–solid phase transitions, but with structural and dynamical signatures that map onto known regions of water’s phase diagram. Using terahertz (THz) absorption spectroscopy and ab initio molecular dynamics, we have investigated the ambient water confined in a supramolecular tetrahedral assembly, and determined that a dynamically distinct network of 9 ± 1 water molecules is present within the nanocavity of the host. The low-frequency absorption spectrum and theoretical analysis of the water in the Ga 4 L 6 12– host demonstrate that the structure and dynamics of the encapsulated droplet is distinct from any known phase of water. A further inference is that the release of the highly unusual encapsulated water droplet creates a strong thermodynamic driver for the high-affinity binding of guests in aqueous solution for the Ga 4 L 6 12– supramolecular construct.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast-irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with small unit cell (~200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of Al 90 Ce 10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al 90 Ce 10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Fast Na diffusion and anharmonic phonon dynamics in superionic Na 3 PS 4

The design of new solid electrolytes (SEs) hinges on identifying and tuning relevant descriptors. Phonons describe the atomic dynamics in crystalline materials and provide a basis to encode possible minimum energy pathways for ion migration, but anharmonic effects can be large in SEs. Identifying and controlling the pertinent phonon modes coupled most strongly with ionic conductivity, and assessing the role of anharmonicity, could therefore pave the way for discovering and designing new SEs via phonon engineering. In this work, we investigate phonons in Na 3 PS 4 and their coupling to fast Na diffusion, using a combination of neutron scattering, ab initio molecular dynamics (AIMD), and extended molecular dynamics based on machine-learned potentials. We identify that anharmonic soft-modes at the Brillouin zone boundary of the anharmonically stabilized cubic phase constitute key phonon modes that control the Na diffusion process in Na 3 PS 4 . We demonstrate how these strongly anharmonic phonon modes enable Na-ions to hop along the minimum energy pathways. Further, the quasi-elastic neutron scattering (QENS) measurements, supplemented with ab initio and machine-learned molecular dynamics simulations, probe the Na diffusivity and diffusion mechanism. These results offer detailed microscopic insights into the dynamic mechanism of fast Na diffusion and provide an avenue to search for further Na solid electrolytes.

42 ENGINEERING↗
DOE OSTI2025

Applications of Nickelate perovskites for neuromorphic computing from electronic structure and Machine Learning

While the limit of Moore's law is presently being reached with current microelectronic technologies, we need to develop new paradigms that overcome this limitation. In that respect, neuromorphic computing is a concept that emulates the neural behavior and response of the human brain, and it has been recognized as a promising alternative approach. In this research project, we will perform multi-fidelity scale bridging to explore the potential use of materials with metal to insulator transition for neuromorphic applications. In particular, rare earth nickelates are promising for such purposes, as the transition in these materials is quite sensitive to a broad set of different external stimuli. Our multi-fidelity approach will bridge the high-fidelity electronic structure calculations with classical potentials. We will bridge dynamical mean field theory with a classical atomistic representation via a deep learning force field. The neural network is trained with energies, charges, and forces obtained by accurate electronic structure theories based on Dynamical Mean Field Theory. The configurational space is generated from known crystal phases, ab initio molecular dynamics with exchange-correlation functionals corrected with the Hubbard model, disordered phases with different concentrations of oxygen vacancies, and nonsymmetrical positions and induced strain by grain interfaces or contact with a substrate. Strategies to train the model with a reduced number of training examples are obtained from active learning methods, and new structures for improving the learning process are generated by using machine learning autoencoders. This classical potential will be validated through a diversity of electronic structure methods and represents an important step to combine the flexibility and accuracy of first-principles with the speed of classical potentials. The generated multi-fidelity surrogate model will be used to understand the role of strain, oxygen vacancies, proton doping, the variation of the crystal phase, substrate effects, vibrational effects as the octahedral rotation, grain boundaries and defect effects on the response of a Metal to Insulator Transition (MIT) in correlated materials. Long time and large-scale simulations will help understand the role of different stimuli to control the hysteresis of the MIT, as it has been experimentally suggested. Selected configurations will be analyzed with higher-level theories to provide an accurate electronic description and to study how the orbitals and charges are rearranged under different conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Bulk and Nanocrystalline Cesium Lead-Halide Perovskites as Seen by Halide Magnetic Resonance

Lead-halide perovskites increasingly mesmerize researchers because they exhibit a high degree of structural defects and dynamics yet nonetheless offer an outstanding (opto)electronic performance on par with the best examples of structurally stable and defect-free semiconductors. This highly unusual feature necessitates the adoption of an experimental and theoretical mindset and the reexamination of techniques that may be uniquely suited to understand these materials. Surprisingly, the suite of methods for the structural characterization of these materials does not commonly include nuclear magnetic resonance (NMR) spectroscopy. The present study showcases both the utility and versatility of halide NMR and NQR (nuclear quadrupole resonance) for probing the structure and structural dynamics of CsPbX 3 (X = Cl, Br, I), in both bulk and nanocrystalline forms. The strong quadrupole couplings, which originate from the interaction between the large quadrupole moments of, e.g., the 35 Cl, 79 Br, and 127 I nuclei, and the local electric-field gradients, are highly sensitive to subtle structural variations, both static and dynamic. The quadrupole interaction can resolve structural changes with accuracies commensurate with synchrotron X-ray diffraction and scattering. It is shown that space-averaged site-disorder is greatly enhanced in the nanocrystals compared to the bulk, while the dynamics of nuclear spin relaxation indicates enhanced structural dynamics in the nanocrystals. The findings from NMR and NQR were corroborated by ab initio molecular dynamics, which point to the role of the surface in causing the radial strain distribution and disorder. These findings showcase a great synergy between solid-state NMR or NQR and molecular dynamics simulations in shedding light on the structure of soft lead-halide semiconductors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Viscosity in water from first-principles and deep-neural-network simulations

Abstract We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew–Burke–Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗