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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

(U) Updated Godiva-IV Benchmark Preview

A note of errata prepended to the Godiva-IV delayed-critical benchmark (HEU-METFAST-086) identifies two corrections that need to be made to the model: The glory hole in the spindle should be made larger, and the height of the safety block should be made smaller (and therefore its density made larger). In addition, the safety block at its full-in position is closer to the inner subassembly plate than was modeled in the benchmark. These changes have been made to HEU-METFAST-086 Case 4 in order to estimate the effect on k eff and on the neutron flux spectrum. Using smaller separation, a smaller safety block, and a larger glory hole caused k eff to increase by 453 ± 1 pcm from the benchmark. The latest nuclear data, ENDF-B/VIII.0, have also been used; this caused k eff to increase another 37 ± 1 pcm. Flux spectra were compared in a modeled fission foil and at three external point detectors. Within the fission foil, using ENDF/B-VIII.0 for Godiva-IV induced changes in the flux spectrum similar in size to the changes due to using smaller separation, a smaller safety block, and a larger glory hole. At the point detectors, using ENDF/B-VIII.0 induced changes in the flux spectrum much larger than those due to changing the model. In other words, the corrections to the benchmark model cause a large increase in k eff , but the changes to the neutron flux spectrum are small compared to those caused by using the latest nuclear data. This study presents a preview of results expected during the reevaluation of the Godiva IV benchmark, but it is not a substitute for the full reevaluation

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Identifying RR Lyrae Variable Stars in Six Years of the Dark Energy Survey

We present a search for RR Lyrae stars using the full six-year data set from the Dark Energy Survey covering ∼5000 deg2 of the southern sky. Using a multistage multivariate classification and light-curve template-fitting scheme, we identify RR Lyrae candidates with a median of 35 observations per candidate. We detect 6971 RR Lyrae candidates out to ∼335 kpc, and we estimate that our sample is >70% complete at ∼150 kpc. We find excellent agreement with other wide-area RR Lyrae catalogs and RR Lyrae studies targeting the Magellanic Clouds and other Milky Way satellite galaxies. We fit the smooth stellar halo density profile using a broken-power-law model with fixed halo flattening (q = 0.7), and we find strong evidence for a break at with an inner slope of and an outer slope of . We use our catalog to perform a search for Milky Way satellite galaxies with large sizes and low luminosities. Using a set of simulated satellite galaxies, we find that our RR Lyrae-based search is more sensitive than those using resolved stellar populations in the regime of large (r h ≳ 500 pc), low-surface-brightness dwarf galaxies. A blind search for large, diffuse satellites yields three candidate substructures. The first can be confidently associated with the dwarf galaxy Eridanus II. The second has a distance and proper motion similar to the ultrafaint dwarf galaxy Tucana II but is separated by ∼5 deg. The third is close in projection to the globular cluster NGC 1851 but is ∼10 kpc more distant and appears to differ in proper motion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Numerical Study and Validation of Melting and Solidification in PCM Embedded Heat Exchangers with Straight Tube

Latent heat thermal energy storage (LHTES) systems have shown great potential to enable reliable use of renewable energy and load shifting. LHTES offer high storage density and release energy at near constant temperature because of its use of phase change materials (PCMs). The cylindrical PCM heat exchangers (PCMHX) are one of the most used technologies due to their simplicity. Numerical models for such PCMHX enable engineers to estimate their performance for different design parameters and operating conditions without having to test them all. However, modeling the phase change phenomena can be challenging. To better understand the difficulties involving accurate modeling of PCMHX, a cylindrical latent storage unit filled with PCM and water as in-tube heat transfer fluid (HTF) is numerically investigated. This paper presents a study based on a 2D-axisymmetric model of a straight tube embedded in PCM in a cylindrical container. CFD is used to study the charging (melting) and discharging (solidification) phenomena. The models are validated against experimental and numerical data from the literature. The predicted local PCM temperature profile over time agrees within 2K compared to the experimental values. The paper also presents a simple method to estimate the melting and solidification phase change temperature range from limited data provided by PCM manufacturers.

PCM, melting, solidification, CFD, heat exchanger↗
DOE OSTI2025

The separatrix electron density in JET, ASDEX upgrade and alcator C-Mod H-mode plasmas: A common evaluation procedure and correlation with engineering parameters

The separatrix electron density is an important parameter for core-edge scenario integration in tokamak devices, as it influences plasma confinement, divertor detachment and disruption avoidance. This quantity has been measured in H-mode discharges on JET, ASDEX Upgrade and Alcator C-Mod by applying the same fitting function to Thomson scattering measurements, and by employing the same analysis technique based on scrape-off layer power balance. To estimate the power crossing the separatrix, the inter-ELM time derivative of the plasma energy d W /d t has been experimentally evaluated and found to be approximately a constant fraction of the absorbed heating power. Correlations between n e,sep and engineering parameters have been investigated, revealing that n e,sep scales with the divertor neutral pressure p 0,div in a similar manner across all devices. Additionally, when n e,sep is normalized to the obtained n e,sep dependency, no clear correlation with the plasma current is found. These observations are in agreement with the 2-point model, which suggests that the upstream separatrix density is mainly set by the recycling at the divertor target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

First-principles study of L -shell iron and chromium opacity at stellar interior temperatures

Recently developed free-energy density functional theory (DFT)-based methodology for optical property calculations of warm dense matter has been applied for studying L-shell opacity of iron and chromium at T = 182 eV. We use Mermin–Kohn–Sham density functional theory with a ground-state and a fully temperature-dependent generalized gradient approximation exchange-correlation (XC) functionals. It is demonstrated that the role of XC at such a high-T is negligible due to the total free-energy of interacting system being dominated by the noninteracting free-energy term in agreement with estimations for the homogeneous electron gas. Furthermore, our DFT predictions are compared to the radiative emissivity and opacity of dense plasmas model, to the real-space Greens function method, and to experimental measurements. Good agreement is found between all three theoretical methods, and in the bound–continuum region for Cr when compared to the experiment, while the discrepancy between direct DFT calculations and the experiment for Fe remains essentially the same as for plasma-physics models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Neural network denoising of x-ray images from high-energy-density experiments

Noise is a consistent problem for x-ray transmission images of High-Energy-Density (HED) experiments because it can significantly affect the accuracy of inferring quantitative physical properties from these images. We consider experiments that use x-ray area backlighting to image a thin layer of opaque material within a physics package to observe its hydrodynamic evolution. The spatial variance of the x-ray transmission across the system due to changing opacity serves as an analog for measuring density in this evolving layer. The noise in these images adds nonphysical variations in measured intensity, which can significantly reduce the accuracy of our inferred densities, particularly at small spatial scales. Denoising these images is thus necessary to improve our quantitative analysis, but any denoising method also affects the underlying information in the image. In this paper, we present a method for denoising HED x-ray images via a deep convolutional neural network model with a modified DenseNet architecture. In our denoising framework, we estimate the noise present in the real (data) images of interest and apply the inferred noise distribution to a set of natural images. These synthetic noisy images are then used to train a neural network model to recognize and remove noise of that character. We show that our trained denoiser network significantly reduces the noise in our experimental images while retaining important physical features.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Microstructures and deformation mechanisms of the medium-entropy alloy (NiCoCr) 76 (Ni 6 AlTi) 3

Here the microstructures and deformation mechanisms of the f.c.c. medium-entropy alloy (MEA) (NiCoCr) 76 (Ni 6 AlTi) 3 has been analyzed after various thermo-mechanical treatments. The solutionized, single-phase MEA, which had a grain size of 93.4 ± 31.9 μm, was cold-rolled (CR) to a 80% thickness reduction after which it showed both a high yield strength (YS) of 1539 MPa and a high ultimate tensile strength (UTS) of 1602 MPa, but an elongation to failure, ε, of only 16%. The CR MEA was then subjected to one of two heat treatments: (1) the CR MEA was recrystallized at 1100°C for 24 h, which produced a single-phase material with 85 ± 61.7 μm grain size that exhibited a much lower YS and UTS of 364 MPa and 747 MPa, respectively, but a much greater ε of 73%; and (2) the MEA was aged at 700°C for 24 h, which produced a fine-grained (1.1 ± 0.9 μm) material and a high volume fraction (0.35) of 12 nm dia. L1 2 nanoparticles that exhibited an excellent combination of strength and ductility, viz., YS~1501 MPa, UTS~1651 MPa, elongation ~26%. The grain boundary strengthening and precipitation strengthening were together estimated to account for ~58% of the YS in the latter material. After deformation, the recrystallized MEA contained numerous stacking faults and a Taylor lattice structure containing domain boundaries and microbands, while the aged MEA exhibited numerous stacking faults and sheared particles. The density of low-angle grain boundaries (LAGBs) in the recrystallized MEA increased by 100 times to 2.2 × 10 –1 μm/μm 2 while the density of CSL Σ3 n boundaries decreased by 71% to 7.8 × 10 –3 μm/μm 2 after deformation. In contrast, the density of LAGBs in the aged MEA increased by 5 times to 2.6 μm/μm 2 while the density of CSL Σ3 n boundaries decreased by 57% to 0.6 μm/μm 2 after deformation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Controlling extrapolations of nuclear properties with feature selection

Predictions of nuclear properties far from measured data are inherently inaccurate because of uncertainties in our knowledge of nuclear forces and in our treatment of quantum many-body effects in strongly-interacting systems. While the model bias can be directly calculated when experimental data is available, only an estimate can be made in the absence of such measurements. Current approaches to compute the estimated bias quickly lose predictive power when their input variables are taken far from the training region, resulting in uncontrolled uncertainties in applications such as nucleosynthesis simulations. In this letter, we present a novel technique to identify the input variables of machine learning algorithms that can provide robust estimates of model bias. Our process is based on selecting input variables, or features, based on their probability distribution functions across the entire nuclear chart. We illustrate our approach on the problem of quantifying the model bias in nuclear binding energies calculated with Density Functional Theory (DFT). We prove that building model biases with only Z and N as features leads to highly unreliable extrapolations. Conversely, we show that proper feature selection can systematically improve theoretical predictions without increasing uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Early calendar life and health prediction of silicon batteries via machine learning with uncertainty quantification

Lithium-ion batteries with silicon anodes promise high energy density but are limited by calendar lifetime. Reducing the long iteration time to obtain experimental results requires predicting calendar lifetime early in a cell's life. In this study, we demonstrate that lightweight machine learning models with feature engineering can provide calendar lifetime estimates from early electrochemical signals. After 1 month of electrochemical aging, the best models achieve 10% error in calendar-life prediction and can separate "bad" from "good" lifetime cells with a mean F1 score of 0.857. As battery systems exhibit inherent variability, four methods for uncertainty quantification are compared, and confidence intervals are demonstrated with an uncertainty of +-3.6 months in lifetime prediction. A feature importance analysis indicates that early patterns in voltage decay are the strongest indicators of calendar lifetime. Finally, this modeling approach has high error when generalizing to new electrode chemistries or testing conditions but with appropriately low confidence.

25 ENERGY STORAGE↗
DOE OSTI2022

Releasing chemical energy in spatiallyprogrammed ferroelectrics

Chemical energy ferroelectrics are generally solid macromolecules showing spontaneous polarization and chemical bonding energy. These materials still suffer drawbacks, including the limited control of energy release rate, and thermal decomposition energy well below total chemical energy. To overcome these drawbacks, we report the integrated molecular ferroelectric and energetic material from machine learning-directed additive manufacturing coupled with the ice-templating assembly. The resultant aligned porous architecture shows a low density of 0.35 g cm -3 , polarization-controlled energy release, and an anisotropic thermal conductivity ratio of 15. Thermal analysis suggests that the chlorine radicals react with macromolecules enabling a large exothermic enthalpy of reaction (6180 kJ kg -1 ). In addition, the estimated detonation velocity of molecular ferroelectrics can be tuned from 6.69 ± 0.21 to 7.79 ± 0.25 km s -1 by switching the polarization state. These results provide a pathway toward spatially programmed energetic ferroelectrics for controlled energy release rates.

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

Selective reduction in epitaxial SrFe 0.5 Co 0.5 O 2.5 and its reversibility

Oxygen-vacancy engineering in transition metal oxides enables programmable functionalities by modulating the valence states and local coordination of constituents. Here, we report the selective reduction of cobalt ions in epitaxial SrFe 0.5 Co 0.5 O 2.5 thin films under reducing gas environments, while iron ions remain unchanged. X-ray absorption spectroscopy reveals an absorption edge shift of 1.65 eV in the Co L-edge upon reduction, and multiplet simulations estimate a decrease in the average Co valence from Co 2.91+ to Co 2.00+ . This site- and element-specific reduction leads to the formation of a structurally distinct oxygen-deficient phase stabilized by oxygen vacancies at tetrahedral sites, as confirmed by density functional theory. Optical spectroscopy reveals an increase in the bandgap from 2.47 eV to 3.04 eV, accompanied by enhanced transparency. Furthermore, simultaneous in situ diffraction and transport measurements confirm fully reversible redox-driven transitions among three phases: reduced defective perovskite, brownmillerite, and oxygen-rich perovskite phases. These findings demonstrate that selective redox control in multi-cation oxides enables the realization of chemically and functionally distinct oxygen-deficient phases.

Fuel cells↗
DOE OSTI2021

Neutron diffraction study of magnetism in van der Waals layered MnBi 2n Te 3n+1

Two-dimensional van der Waals MnBi 2 n Te 3 n +1 ( n = 1, 2, 3, 4) compounds have been recently found to be intrinsic magnetic topological insulators rendering quantum anomalous Hall effect and diverse topological states. Here, we summarize and compare the crystal and magnetic structures of this family, and discuss the effects of chemical composition on their magnetism. We found that a considerable fraction of Bi occupies at the Mn sites in MnBi 2 n Te 3 n +1 ( n = 1, 2, 3, 4) while there is no detectable Mn at the non-magnetic atomic sites within the resolution of neutron diffraction experiments. The occupancy of Mn monotonically decreases with the increase of n . The polarized neutron diffraction on the representative MnBi 4 Te 7 reveals that its magnetization density is exclusively accumulated at the Mn site, in good agreement with the results from the unpolarized neutron diffraction. The defects of Bi at the Mn site naturally explain the continuously reduced saturated magnetic moments from n = 1 to n = 4. The experimentally estimated critical exponents of all the compounds generally suggest a three-dimensional character of magnetism. Our work provides material-specified structural parameters that may be useful for band structure calculations to understand the observed topological surface states and for designing quantum magnetic materials through chemical doping.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Validating sequential Monte Carlo for gravitational-wave inference

Nested sampling (NS) is the preferred stochastic sampling algorithm for gravitational-wave inference for compact binary coalescences. It can handle the complex nature of the gravitational-wave likelihood surface and provides an estimate of the Bayesian model evidence. However, there is another class of algorithms that meets the same requirements, but has not been used for gravitational-wave analyses: sequential Monte Carlo (SMC), an extension of importance sampling that maps samples from an initial density to a target density via a series of intermediate densities. In this work, we validate a type of SMC algorithm, called persistent sampling (PS), for gravitational-wave inference. We consider a range of different scenarios including binary black holes and binary neutron stars and real and simulated data and show that PS produces results that are consistent with NS whilst being, on average, 2 times more efficient and 2.74 times faster. This demonstrates that PS is a viable alternative to NS that should be considered for future gravitational-wave analyses.

black hole mergers↗
DOE OSTI2022

Multimodal correlative imaging and modelling of phosphorus uptake from soil by hyphae of mycorrhizal fungi

Phosphorus (P) is essential for plant growth. Arbuscular mycorrhizal fungi (AMF) aid its uptake by acquiring P from sources distant from roots in return for carbon. Little is known about how AMF colonise soil pore-space, and models of AMF-enhanced P-uptake are poorly validated. We used synchrotron X-ray computed tomography to visualize mycorrhizas in soil and synchrotron X-ray fluorescence/X-ray absorption near edge structure (XRF/XANES) elemental mapping for P, sulphur (S) and aluminium (Al) in combination with modelling. We found that AMF inoculation had a suppressive effect on colonisation by other soil fungi and identified differences in structure and growth rate between hyphae of AMF and nonmycorrhizal fungi. Our results showed that AMF co-locate with areas of high P and low Al, and preferentially associate with organic-type P species over Al-rich inorganic P. We discovered that AMF avoid Al-rich areas as a source of P. Sulphur-rich regions were found to be correlated with higher hyphal density and an increased organic-associated P-pool, whilst oxidized S-species were found close to AMF hyphae. Increased S oxidation close to AMF suggested the observed changes were microbiome-related. Our experimentally-validated model led to an estimate of P-uptake by AMF hyphae that is an order of magnitude lower than rates previously estimated – a result with significant implications for the modelling of plant–soil–AMF interactions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Oxygen torus and its coincidence with EMIC wave in the deep inner magnetosphere: Van Allen Probe B and Arase observations

We investigate the longitudinal structure of the oxygen torus in the inner magnetosphere for a specific event found on 12 September 2017, using simultaneous observations from the Van Allen Probe B and Arase satellites. It is found that Probe B observed a clear enhancement in the average plasma mass (M) up to 3–4 amu at L = 3.3–3.6 and magnetic local time (MLT) = 9.0 h. In the afternoon sector at MLT ~ 16.0 h, both Probe B and Arase found no clear enhancements in M. This result suggests that the oxygen torus does not extend over all MLT but is skewed toward the dawn. Since a similar result has been reported for another event of the oxygen torus in a previous study, a crescent-shaped torus or a pinched torus centered around dawn may be a general feature of the O + density enhancement in the inner magnetosphere. We newly find that an electromagnetic ion cyclotron (EMIC) wave in the H + band appeared coincidently with the oxygen torus. From the lower cutoff frequency of the EMIC wave, the ion composition of the oxygen torus is estimated to be 80.6% H + , 3.4% He + , and 16.0% O + . According to the linearized dispersion relation for EMIC waves, both He + and O + ions inhibit EMIC wave growth and the stabilizing effect is stronger for He + than O + . Therefore, when the H + fraction or M is constant, the denser O + ions are naturally accompanied by the more tenuous He + ions, resulting in a weaker stabilizing effect (i.e., larger growth rate). From the Probe B observations, we find that the growth rate becomes larger in the oxygen torus than in the adjacent regions in the plasma trough and the plasmasphere.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles

The space-borne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006. To build long-term records from space-borne lidars useful for climate studies, one has to understand the differences between successive space lidars operating at different wavelengths, flying on different orbits, and using different viewing geometries, receiving paths, and detectors. In this article, we compare the results of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidars for the period from 28 June to 31 December 2019. First, we build a dataset of ALADIN–CALIOP collocated profiles (Δdist<1°; Δtime<6 h). Then we convert ALADIN's 355 nm particulate backscatter and extinction profiles into the scattering ratio vertical profiles SR(z) at 532 nm using molecular density profiles from Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5 DAS). And finally, we build the CALIOP and ALADIN globally gridded cloud fraction profiles CF(z) by applying the same cloud detection threshold to the SR(z) profiles of both lidars at the same spatial resolution. Before comparing the SR(z) and CF(z) profiles retrieved from the two analyzed lidar missions, we performed a numerical experiment to estimate the best achievable cloud detection agreement CDA norm (z) considering the differences between the instruments. We define CDA norm (z) in each latitude–altitude bin as the occurrence frequency of cloud layers detected by both lidars, divided by a cloud fraction value for the same latitude–altitude bin. We simulated the SR(z) and CF(z) profiles that would be observed by these two lidars if they were flying over the same atmosphere predicted by a global model. By analyzing these simulations, we show that the theoretical limit for CDA$_{norm}^{theor}$(z) for a combination of ALADIN and CALIOP instruments is equal to 0.81±0.07 at all altitudes. In other words, 19 % of the clouds cannot be detected simultaneously by two instruments due to said differences. The analyses of the actual observed CALIOP–ALADIN collocated dataset containing ~78 000 pairs of nighttime SR(z) profiles revealed the following points: (a) the values of SR(z) agree well up to ~3 km height. (b) The CF(z) profiles show agreement below ~3 km, where ~80 % of the clouds detected by CALIOP are detected by ALADIN as expected from the numerical experiment. (c) Above this height, the CDA$_{norm}^{obs}$(z) reduces to ~50 %. (d) On average, better sensitivity to lower clouds skews ALADIN's cloud peak height in pairs of ALADIN–CALIOP profiles by ~0.5±0.6 km downwards, but this effect does not alter the heights of polar stratospheric clouds and high tropical clouds thanks to their strong backscatter signals. (e) The temporal evolution of the observed CDA$_{norm}^{obs}$(z) does not reveal any statistically significant change during the considered period. This indicates that the instrument-related issues in ALADIN L0/L1 have been mitigated, at least down to the uncertainties of the following CDA$_{norm}^{obs}$(z) values: 68±12 %, 55±14 %, 34±14 %, 39±13 %, and 42±14 % estimated at 0.75, 2.25, 6.75, 8.75, and 10.25 km, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strong Rashba-Dresselhaus Effect in Nonchiral 2D Ruddlesden-Popper Perovskites

Chirality transfer from organic chiral molecules to lead halides is theorized as the origin of the strong Rashba-Dresselhaus effect causing large circular dichroism (CD) and circularly polarized luminescence (CPL) in metal halide perovskites (MHPs). In this report a concrete empirical evidence is provided that such strong CD and CPL can occur even in nonchiral 2D Ruddlesden-Popper perovskites (RPPs) (BA) 2 (MA) n-1 Pb n I 3n+1 (where MA = CH 3 NH 3 and BA = CH 3 (CH 2 ) 3 NH 3 ). The CD and CPL responses occurring at the excitonic transition of the MHPs are strongest (≈100 mdeg and 4.8%, respectively) when a single lead halide octahedral [PbI 6 ] 4- layer is repeatedly stacked between two nonchiral molecules BA + (n = 1). However, they are rapidly quenched as n increases. It is hypothesized that strong Rashba-Dresselhaus splitting in the 2D RPPs originates the strong CD and CPL signatures. Density functional theory calculations reveal that the large interlayer distortions in the inorganic layers at the organic/inorganic interface give rise to the strong Rashba-Dresselhaus splitting. A Rashba-Dresselhaus field of ≈600 and ≈50 mT for n = 1 and 2, respectively, is estimated by magnetic circular dichroism spectroscopy. The studies may have significant impact on designing 2D RPPs with large Rashba-Dresselhaus effects at room temperature for spintronic applications.

circular dichroism↗