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Spin relaxation of electrons in materials involve both reversible dephasing and irreversible decoherence processes. Their interplay leads to a complex dependence of spin relaxation times on the direction and magnitude of magnetic fields, relevant for spintronics and quantum information applications. Here, we use real-time first-principles density matrix dynamics simulations to directly simulate Hahn echo measurements, disentangle dephasing from decoherence, and predict T1, T2 and T2* spin lifetimes. We show that g-factor fluctuations lead to non-trivial magnetic field dependence of each of these lifetimes in inversion-symmetric crystals of CsPbBr3 and silicon, even when only intrinsic spin-phonon scattering is present. Most importantly, fluctuations in the off-diagonal components of the g-tensor lead to a strong magnetic field dependence of even the T1 lifetime in silicon. Our calculations elucidate the detailed role of anisotropic g-factors in determining the spin dynamics even in simple, low spin-orbit coupling materials such as silicon.

With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type of compression - one that represents the model in a compact form and is coupled with a decompression algorithm that returns it to its original form and size - namely lossless compression. We present ZipNN a lossless compression tailored to neural networks. Somewhat surprisingly, we show that specific lossless compression can gain significant network and storage reduction on popular models, often saving 33% and at times reducing over 50% of the model size. We investigate the source of model compressibility and introduce specialized compression variants tailored for models that further increase the effectiveness of compression. On popular models (e.g. Llama 3) ZipNN shows space savings that are over 17% better than vanilla compression while also improving compression and decompression speeds by 62%. We estimate that these methods could save over an ExaByte per month of network traffic downloaded from a large model hub like Hugging Face., Comment: arXiv admin note: substantial text overlap with arXiv:2404.15198

To improve relevance scoring on Pinterest Search, we integrate Large Language Models (LLMs) into our search relevance model, leveraging carefully designed text representations to predict the relevance of Pins effectively. Our approach uses search queries alongside content representations that include captions extracted from a generative visual language model. These are further enriched with link-based text data, historically high-quality engaged queries, user-curated boards, Pin titles and Pin descriptions, creating robust models for predicting search relevance. We use a semi-supervised learning approach to efficiently scale up the amount of training data, expanding beyond the expensive human labeled data available. By utilizing multilingual LLMs, our system extends training data to include unseen languages and domains, despite initial data and annotator expertise being confined to English. Furthermore, we distill from the LLM-based model into real-time servable model architectures and features. We provide comprehensive offline experimental validation for our proposed techniques and demonstrate the gains achieved through the final deployed system at scale., Comment: CIKM 2024 Workshop on Industrial Recommendation Systems

We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that require a global shape encoding, we observe that detail-preserving deformations can be estimated reliably without any global context in certain scenarios. Building on this intuition, we leverage Jacobians defined in a one-ring neighborhood as a coarse representation of the deformation. Using this as the input to our neural network, we apply a series of MLPs combined with feature smoothing to learn the Jacobian corresponding to the detail-preserving deformation, from which the embedding is recovered by the standard Poisson solve. Crucially, by removing the dependence on a global encoding, every \textit{point} becomes a training example, making the supervision particularly lightweight. Moreover, when trained on a class of shapes, our approach demonstrates remarkable generalization across different object categories. Equipped with this novel network, we explore three main tasks: refining an approximate shape correspondence, unsupervised deformation and mapping, and shape editing. Our code is made available at https://github.com/sentient07/LJN

The hybrid organic-inorganic halide perovskite (HOIP), for example MAPbBr3, exhibits extended spin lifetime and apparent spin lifetime anisotropy in experiments. The underlying mechanisms of these phenomena remain illusive. By utilizing our first-principles densitymatrix dynamics approach with quantum scatterings including electron-phonon and electronelectron interactions and self-consistent spinorbit coupling, we present temperature- and magnetic field-dependent spin lifetimes in hybrid perovskites, in agreement with experimental observations. For centrosymmetric hybrid perovskite MAPbBr3, the experimentally observed spin lifetime anisotropy is mainly attributed to the dynamical Rashba effect arising from the interaction between organic and inorganic components and the rotation of the organic cation. For noncentrosymmetric perovskite, such as MPSnBr3, we found persistent spin helix texture at the conduction band minimum, which significantly enhances the spin lifetime anisotropy. Our study provides theoretical insight to spin dynamics in HOIP and strategies for controlling and optimizing spin transport.

Exploring nonequilibrium hot carriers from plasmonic metal nanostructures is a dynamic field in optoelectronics, driving photochemical reactions such as solar fuel generation. The hot carrier injection mechanism and the reaction rate are highly impacted by the metal/molecule interaction. However, determining the primary type of the reaction and thus the injection mechanism of the hot carriers has remained elusive. In this work, we reveal an electron injection mechanism deviating from a purely outersphere process for the reduction of ferricyanide redox molecule in a gold/p-type gallium nitride (Au/p- GaN) photocathode system. Combining our experimental approach with ab initio simulations, we discover that the efficient inner-sphere transfer of low-energy electrons leads to a continuous enhancement in the photocathode device performance in the interband regime. These findings provide important mechanistic insights, showing our methodology as a powerful tool for analyzing and engineering hot-carrier-driven processes in plasmonic photocatalytic systems and optoelectronic devices.

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings., Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonilla

The accuracy of density-functional theory (DFT) is determined by the quality of the approximate functionals, such as exchange-correlation in electronic DFT and the excess functional in the classical DFT formalism of fluids. The exact functional is highly nonlocal for both electrons and fluids, yet most approximate functionals are semi-local or nonlocal in a limited weighted-density form. Machine-learned (ML) nonlocal density-functional approximations are promising in both electronic and classical DFT, but have so far employed disparate approaches with limited generality. Here, we formulate a universal approximation framework and training protocol for nonlocal ML functionals, combining features of equivariant convolutional neural networks and the weighted-density approximation. We prototype this approach for several 1D and quasi-1D problems and demonstrate that a functional with exactly the same hyperparameters achieves excellent accuracy for the hard-rod fluid, the inhomogeneous Ising model, the exact exchange functional for electrons, the electron kinetic energy functional for orbital-free DFT, as well as for liquid water with 1D inhomogeneities. These results lay the foundation for a universal ML approach to exact 3D functionals spanning electronic and classical DFT., Comment: 12 pages, 7 figures

Developing theoretical understanding of complex reactions and processes at interfaces requires using methods that go beyond semilocal density functional theory to accurately describe the interactions between solvent, reactants and substrates. Methods based on many-body perturbation theory, such as the random phase approximation (RPA), have previously been limited due to their computational complexity. However, this is now a surmountable barrier due to the advances in computational power available, in particular through modern GPU-based supercomputers. In this work, we describe the implementation of RPA calculations within BerkeleyGW and show its favorable computational performance on large complex systems relevant for catalysis and electrochemistry applications. Our implementation builds off of the static subspace approximation which, by employing a compressed representation of the frequency dependent polarizability, enables the evaluation of the RPA correlation energy with significant acceleration and systematically controllable accuracy. We find that the computational cost of calculating the RPA correlation energy scales only linearly with system size for systems containing up to 50 thousand bands, and is expected to scale quadratically thereafter. We also show excellent strong scaling results across several supercomputers, demonstrating the performance and portability of this implementation., Comment: 10 pages, 5 figures

We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage., Comment: 24 pages, 8 figures

Learning-based isosurface extraction methods have recently emerged as a robust and efficient alternative to axiomatic techniques. However, the vast majority of such approaches rely on supervised training with axiomatically computed ground truths, thus potentially inheriting biases and data artifacts of the corresponding axiomatic methods. Steering away from such dependencies, we propose a self-supervised training scheme for the Neural Dual Contouring meshing framework, resulting in our method: Self-Supervised Dual Contouring (SDC). Instead of optimizing predicted mesh vertices with supervised training, we use two novel self-supervised loss functions that encourage the consistency between distances to the generated mesh up to the first order. Meshes reconstructed by SDC surpass existing data-driven methods in capturing intricate details while being more robust to possible irregularities in the input. Furthermore, we use the same self-supervised training objective linking inferred mesh and input SDF, to regularize the training process of Deep Implicit Networks (DINs). We demonstrate that the resulting DINs produce higher-quality implicit functions, ultimately leading to more accurate and detail-preserving surfaces compared to prior baselines for different input modalities. Finally, we demonstrate that our self-supervised losses improve meshing performance in the single-view reconstruction task by enabling joint training of predicted SDF and resulting output mesh. We open-source our code at https://github.com/Sentient07/SDC

With the growth of model sizes and scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast literature about reducing model sizes, we investigate a more traditional type of compression -- one that compresses the model to a smaller form and is coupled with a decompression algorithm that returns it to its original size -- namely lossless compression. Somewhat surprisingly, we show that such lossless compression can gain significant network and storage reduction on popular models, at times reducing over $50\%$ of the model size. We investigate the source of model compressibility, introduce compression variants tailored for models and categorize models to compressibility groups. We also introduce a tunable lossy compression technique that can further reduce size even on the less compressible models with little to no effect on the model accuracy. We estimate that these methods could save over an ExaByte per month of network traffic downloaded from a large model hub like HuggingFace.

Spintronic logic devices require efficient spin-charge interconversion: converting charge current to spin current and spin current to charge current. In spin-orbit materials that are regarded as the most promising candidate for spintronic logic devices, one mechanism that is responsible for spin-charge interconversion is Edelstein and inverse Edelstein effects based on spin-momentum locking in materials with Rashba-type spin-orbit coupling. Over last decade, there has been rapid progresses for increasing interconversion efficiencies due to the Edelstein effect in a few Rashba-Dresselhaus materials and topological insulators, making Rashba spin-momentum locking a promising technological solution for spin-orbit logic devices. However, despite the rapid progress that leads to high spin-charge interconversion efficiency at cryogenic temperatures, the room-temperature efficiency needed for technological applications is still low. This paper presents our understanding on the challenges and opportunities in searching for Rashba-Dresselhaus materials for efficient spin-charge interconversion at room temperature by focusing on materials properties such as Rashba coefficients, momentum relaxation times, spin-momentum locking relations and electrical conductivities., Comment: 5 figures

Topological materials confined in one-dimension (1D) can transform computing technologies, such as 1D topological semimetals for nanoscale interconnects and 1D topological superconductors for fault-tolerant quantum computing. As such, understanding crystallization of 1D-confined topological materials is critical. Here, we demonstrate 1D-confined crystallization routes during template-assisted nanowire synthesis where we observe diameter-dependent phase selectivity for topological metal tungsten phosphides. A phase bifurcation occurs to produce tungsten monophosphide and tungsten diphosphide at the cross-over nanowire diameter of ~ 35 nm. Four-dimensional scanning transmission electron microscopy was used to identify the two phases and to map crystallographic orientations of grains at a few nm resolution. The 1D-confined phase selectivity is attributed to the minimization of the total surface energy, which depends on the nanowire diameter and chemical potentials of precursors. Theoretical calculations were carried out to construct the diameter-dependent phase diagram, which agrees with experimental observations. Our find-ings suggest a new crystallization route to stabilize topological materials confined in 1D., Comment: 5 figures

Harnessing non-equilibrium hot carriers from plasmonic metal nanostructures constitutes a vibrant research field. It promises to enable control of activity and selectivity of photochemical reactions, especially for solar fuel generation. However, a comprehensive understanding of the interplay of plasmonic hot carrier-driven processes in metal/semiconducting heterostructures has remained elusive. In this work, we reveal the complex interdependence between plasmon excitation, hot carrier generation, transport and interfacial collection in plasmonic photocatalytic devices, uniquely determining the charge injection efficiencies at the solid/solid and solid/liquid interfaces. Interestingly, by measuring the internal quantum efficiency of ultrathin (14 to 33 nm) single-crystalline plasmonic gold (Au) nanoantenna arrays on titanium dioxide substrates, we find that the performance of the device is governed by hot hole collection at the metal/electrolyte interface. In particular, by combining a solid- and liquid-state experimental approach with ab initio simulations, we show a more efficient collection of high-energy d-band holes traveling in [111] orientation, resulting in a stronger oxidation reaction at the {111} surfaces of the nanoantenna. These results thus establish new guidelines for the design and optimization of plasmonic photocatalytic systems and optoelectronic devices.

Luminescence constitutes a unique source of insight into hot carrier processes in metals, including those in plasmonic nanostructures used for sensing and energy applications. However, being weak in nature, metal luminescence remains poorly understood, its microscopic origin strongly debated, and its potential for unravelling nanoscale carrier dynamics largely unexploited. Here, we reveal quantum-mechanical effects emanating in the luminescence from thin monocrystalline gold flakes. Specifically, we present experimental evidence, supported by first-principles simulations, to demonstrate its photoluminescence origin when exciting in the interband regime. Our model allows us to identify changes to the measured gold luminescence due to quantum-mechanical effects as the gold film thickness is reduced. Excitingly, such effects are observable in the luminescence signal from flakes up to 40 nm in thickness, associated with the out-of-plane discreteness of the electronic band structure near the Fermi level. We qualitatively reproduce the observations with first-principles modelling, thus establishing a unified description of luminescence in gold and enabling its widespread application as a probe of carrier dynamics and light-matter interactions in this material. Our study paves the way for future explorations of hot-carriers and charge-transfer dynamics in a multitude of material systems., Comment: Main text 21 pages and 4 figures. Supplemental Information 33 pages and 17 figures

Precise prediction of phase diagrams in molecular dynamics (MD) simulations is challenging due to the simultaneous need for long time scales, large length scales and accurate interatomic potentials. We show that thermodynamic integration (TI) from low-cost force fields to neural network potentials (NNPs) trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid-liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange-correlation functionals for predicting the NaCl phase boundary, and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid-liquid phase boundaries for any material at an ab-initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

We introduce a fully ab initio theory for inelastic scattering of any atom from any surface exciting single phonons, and apply the theory to helium scattering from Nb(100). The key aspect making our approach general is a direct first-principles evaluation of the scattering atom-electron vertex. By correcting misleading results from current state-of-the-art theories, this fully ab initio approach will be critical in guiding and interpreting experiments that adopt next-generation, non-destructive atomic beam scattering., Comment: 6 pages, 5 figures

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical and biological processes at such interfaces. Classical molecular dynamics simulations have been applied extensively to simulate the response of inhomogeneous fluids directly, and as inputs to classical density functional theory, but are limited by the accuracy of the underlying empirical force fields. Here, we deploy neural network potentials (NNPs) trained to \emph{ab initio} simulations to accurately predict the inhomogeneous response of two widely different fluids: liquid water and molten NaCl. Although the advantages of NNPs is that they can be readily trained to model complex systems, one limitation in modeling the inhomogeneous response of liquids is the need for including the correct configurations of the system in the training data. Therefore, first we establish protocols, based on molecular dynamics simulations in external atomic potentials, to sufficiently sample the correct configurations of inhomogeneous fluids. We show that NNPs trained to inhomogeneous fluid configurations can predict several properties such as the density response, surface tension and size-dependent cavitation free energies in water and molten NaCl corresponding to \emph{ab initio} interactions, more accurately than with empirical force fields. This work therefore provides a first demonstration and framework for extracting the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

Plasmons, collective excitations of electrons in solids, are associated with strongly confined electromagnetic fields, with wavelengths far below the wavelength of photons in free space. This strong confinement promises the realization of optoelectronic devices that could bridge the size difference between photonic and electronic devices. However, despite decades of research in plasmonics, many applications remain limited by plasmonic losses, thus motivating a search for new engineered plasmonic materials with lower losses. A promising pathway for low-loss plasmonic materials is the engineering of materials with flat and energetically isolated metallic bands, which can strongly limit phonon-assisted optical losses, a major contributor to short plasmonic lifetimes. Such electronic band structures may be created by judiciously introducing an ordered lattice of defects in an insulating host material. Here, we explore this approach, presenting several low-loss, highly-confined, and tunable plasmonic materials based on arrays of carbon substitutions in hexagonal boron nitride (hBN) monolayers. From our first-principles calculations based on density functional theory (DFT), we find plasmonic structures with mid-infrared plasmons featuring very high confinements ($\lambda_{\text{vacuum}}/\lambda_{\text{plasmon}}$ exceeding 2000) and quality factors in excess of 1000. We provide a systematic explanation of how crystal structure, electronic bandwidth, and many-body effects affect the plasmonic dispersions and losses of these materials. The results are thus of relevance to low-loss plasmon engineering in other flat band systems.

Divalent ions, such as Mg, Ca, and Zn, are being considered as competitive, safe, and earth-abundant alternatives to Li-ion electrochemistry. However, the challenge remains to match electrode and electrolyte materials that stably cycle with these new formulations, based primarily on controlling interfacial phenomena. We explore the formation of electroactive species in the electrolyte Ca(BH$_4$)$_2$ in THF through molecular dynamics simulation. Free-energy analysis indicates that this electrolyte has a majority population of neutral Ca dimers and monomers, albeit with diverse molecular conformations as revealed by unsupervised learning techniques, but with an order of magnitude lower concentration of possibly electroactive charged species, such as the monocation, CaBH$_4^+$ , which we show is produced via disproportionation of neutral Ca(BH$_4$)$_2$ complexes. Dense layering of THF molecules within 1 nm of the electrode surface (modeled here using graphite) hinders the approach of reducible species to within 0.6 nm and instead enhances the local concetration of species in a narrow intermediate-density layer from 0.7-0.9 nm. A dramatic increase in the monocation population in this intermediate layer is induced at negative bias, supplied by local dimer disproportionation. We see no evidence to support any functional role of fully-solvated Ca$^{2+}$ in the electrochemical activity of this electrolyte. The consequences for performance and alternative formulations are discussed in light of this molecular-scale insight.

Chiral crystals show promise for spintronic technologies on account of their high spin selectivity, which has led to significant recent interest in quantitative characterization and first-principles prediction of their spin-optoelectronics properties. Here, we outline a computational framework for efficient ab-initio calculations of circular dichroism (CD) in crystalline materials. We leverage direct calculations of orbital angular momentum and quadrupole matrix element calculations in density-functional theory (DFT) and Wannier interpolation to calculate CD in complex materials, removing the need for band convergence and accelerating Brillouin-zone convergence compared to prior approaches. We find strong agreement with measured CD signals in molecules and crystals ranging in complexity from small bulk unit cells to 2D hybrid perovskites, and show the importance of the quadrupole contribution to the anisotropic CD in crystals. Spin-orbit coupling affects the CD of crystals with heavier atoms, as expected, but this is primarily due to changes in the electronic energies, rather than due to direct contributions from the spin matrix elements. We showcase the capability to predict CD for complex structures on a 2D hybrid perovskite, finding strong orientation dependence and identifying the eigen-directions of the unit cell with the strongest CD. We additionally decompose CD into separate contributions from inorganic, organic, and mixed organic-inorganic transitions, finding the chiral molecules to dominate the CD, with the inorganic lattice contributing at higher frequencies in specific directions. This unprecedented level of detail in CD predictions in crystals will facilitate experimental development of complex chiral crystals for spin selectivity., Comment: 12 pages, 6 figures

Molecular dynamics (MD) simulations present a data-mining challenge, given that they can generate a considerable amount of data but often rely on limited or biased human interpretation to examine their information content. By not asking the right questions of MD data we may miss critical information hidden within it. We combine dimensionality reduction (UMAP) and unsupervised hierarchical clustering (HDBSCAN) to quantitatively characterize the coordination environment of chemical species within MD data. By focusing on local coordination, we significantly reduce the amount of data to be analyzed by extracting all distinct molecular formulas within a given coordination sphere. We then efficiently combine UMAP and HDBSCAN with alignment or shape-matching algorithms to classify these formulas into distinct structural isomer families. The outcome is a quantitative mapping of the multiple coordination environments present in the MD data. The method was employed to reveal details of cation coordination in electrolytes based on molecular liquids., Comment: 15 pages, 12 figures

Carrier transport in materials is often diffusive due to momentum-relaxing scattering with phonons and defects. Suppression of momentum-relaxing scattering can lead to the ballistic and hydrodynamic transport regimes, wherein complex non-Ohmic current flow patterns, including current vortices, can emerge. In the ballistic regime addressed here, transverse magnetic focusing is habitually understood in a familiar single-particle picture of carriers injected from a source, following ballistic cyclotron orbits and reaching a detector. We report on a distinctive nonlocal magnetoresistance phenomenon exclusive to fermions, in an enclosed mesoscopic geometry wherein transverse focusing magnetoresistance peaks also occur at values of the cyclotron diameter that are incommensurate with the distance between the source and detector. In low-temperature experiments and simulations using GaAs/AlGaAs heterostructures with high electron mobility, we show that the peaks occur independently of the location of the detector, and only depend on the source-drain separation. We reproduce the experimental findings using simulations of ballistic transport in both semiclassical and quantum-coherent transport models. The periodicity of magnetic field at which the peaks occur is matched to the lithographically defined device scale. It is found that, unlike in transverse magnetic focusing, the magnetoresistance structure cannot be attributed to any set of ordered single-particle trajectories but instead requires accounting for the collective dynamics of the fermion distribution and of all particle trajectories. The magnetoresistance is further associated with current flow vorticity, a collective phenomenon., Comment: 11 figures

In this work we present a novel approach for computing correspondences between non-rigid objects, by exploiting a reduced representation of deformation fields. Different from existing works that represent deformation fields by training a general-purpose neural network, we advocate for an approximation based on mesh-free methods. By letting the network learn deformation parameters at a sparse set of positions in space (nodes), we reconstruct the continuous deformation field in a closed-form with guaranteed smoothness. With this reduction in degrees of freedom, we show significant improvement in terms of data-efficiency thus enabling limited supervision. Furthermore, our approximation provides direct access to first-order derivatives of deformation fields, which facilitates enforcing desirable regularization effectively. Our resulting model has high expressive power and is able to capture complex deformations. We illustrate its effectiveness through state-of-the-art results across multiple deformable shape matching benchmarks. Our code and data are publicly available at: https://github.com/Sentient07/DeformationBasis.

Protected surface states arising from non-trivial bandstructure topology in semimetals can potentially enable new device functionalities in compute, memory, interconnect, sensing, and communication. This necessitates a fundamental understanding of surface-state transport in nanoscale topological semimetals. Here, we investigate quantum transport in a prototypical topological semimetal NbAs to evaluate the potential of this class of materials for beyond-Cu interconnects in highly-scaled integrated circuits. Using density functional theory (DFT) coupled with non-equilibrium Green's function (NEGF) calculations, we show that the resistance-area RA product in NbAs films decreases with decreasing thickness at the nanometer scale, in contrast to a nearly constant RA product in ideal Cu films. This anomalous scaling originates from the disproportionately large number of surface conduction states which dominate the ballistic conductance by up to 70% in NbAs thin films. We also show that this favorable RA scaling persists even in the presence of surface defects, in contrast to RA sharply increasing with reducing thickness for films of conventional metals, such as Cu, in the presence of surface defects. These results underscore the promise of topological semimetals like NbAs as future back-end-of-line (BEOL) interconnect metals.

Spintronics in halide perovskites has drawn significant attention in recent years, due to highly tunable spin-orbit fields and intriguing interplay with lattice symmetry. Spin lifetime -- a key parameter that determines the applicability of materials for spintronics and spin-based quantum information applications -- has been extensively measured in halide perovskites, but not yet assessed from first-principles calculations. Here, we leverage our recently-developed \emph{ab initio} density-matrix dynamics framework to compute the spin relaxation time ($T_{1}$) and ensemble spin dephasing time ($T_{2}^{*}$) in a prototype halide perovskite, namely CsPbBr$_{3}$ with self-consistent spin-orbit coupling (SOC) and quantum descriptions of the electron scattering processes. We also implement the Land\'e $g$-factor for solids from first principles and take it into account in our dynamics, which is required to accurately capture spin dephasing at external magnetic fields. We thereby predict intrinsic spin lifetimes as an upper bound for experiments, identify the dominant spin relaxation pathways, and evaluate the dependence on temperature, external fields, carrier density,and impurities. Importantly, we find that the Fr{\"o}hlich interaction that dominates carrier relaxation contributes negligibly to spin relaxation, consistent with the spin-conserving nature of this interaction. We investigated the effect of spin-orbit field with inversion asymmetry on spin lifetime, and we demonstrated from our calculation, persistent spin helix can enhance spin lifetime when the spin-split is large, but it can not be realized by Rashba SOC. Our theoretical approach may lead to new strategies to optimize spin and carrier transport properties in spintronics and quantum information applications., Comment: 10 pages, 6 figures

While pre-trained large-scale deep models have garnered attention as an important topic for many downstream natural language processing (NLP) tasks, such models often make unreliable predictions on out-of-distribution (OOD) inputs. As such, OOD detection is a key component of a reliable machine-learning model for any industry-scale application. Common approaches often assume access to additional OOD samples during the training stage, however, outlier distribution is often unknown in advance. Instead, we propose a post hoc framework called POORE - POsthoc pseudo-Ood REgularization, that generates pseudo-OOD samples using in-distribution (IND) data. The model is fine-tuned by introducing a new regularization loss that separates the embeddings of IND and OOD data, which leads to significant gains on the OOD prediction task during testing. We extensively evaluate our framework on three real-world dialogue systems, achieving new state-of-the-art in OOD detection., Comment: Work in progress