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Selecting materials for hybrid cathodes for batteries, which combine intercalation and conversion materials, has gained interest due to their unique synergistic properties, which are not achievable by homogeneous materials. Here, we present a data-driven, chemistry-agnostic, inverse material design framework to discover hybrid cathode materials (HCMs) for metal-based batteries. This framework systematically explores the material space for any working ion, evaluates the stability of candidates, and identifies the growth modes and adsorption of components for a stable hybrid cathode. To demonstrate the framework's application, we conducted a case study aimed at discovering HCMs with an average gravimetric energy density that exceeds the widely used high-energy NMC333 cathode material. The framework identified LiCr$_4$GaS$_8$-Li$_2$S as a promising HCM, achieving an average energy density of 1,424 Wh/kg (on a lithiated cathode basis), surpassing NMC333's theoretical maximum of 1,028 Wh/kg. Additional features of the identified material include: (1) thermodynamically stable lithiated and delithiated intercalation and conversion phases; (2) minimal volume change upon (de)lithiation, which mitigates the conversion material's high volume change; (3) high energy density, compensating for the lower energy density of the intercalation material; (4) the intercalation component acting as both a conductive additive and a sulfur immobilizer while contributing to the total energy density; (5) serving as an ideal support for soft sulfur species; and (6) potential improvements in lifespan, self-discharge, mechanical integrity, and capacity fading compared to conventional Li-S batteries. The developed framework was crucial in exploring materials within the vast potential HCM space with predefined battery material design objectives.

Ultrafast optics is driven by a myriad of complex nonlinear dynamics. The ubiquitous presence of governing equations in the form of partial integro-differential equations (PIDE) necessitates the need for advanced computational tools to understand the underlying physical mechanisms. From the experimental perspective, signal-to-noise ratio and availability of measurable data, accounts for a bottle neck in numerical and data-driven modeling methods. In this paper we extend the application of the physics informed neural network (PINN) architecture to include prior knowledge in both the physical and Fourier domain. We demonstrate our Fourier Domain PINN (FD-PINN) in two distinct forms. The Continuous time FD-PINN is used to predict accurate solutions to the Generalized Pulse Propagation Equation, which includes the complete delayed nonlinear response, in the data-starved and noisy regime. We extend the architecture to the Discrete time FD-PINN to recover the delayed-response physics from spatially separated measurement points. We conducted the first systematic study of the effect of SNR on the spatiotemporal field prediction as well as physics discovery. Our architecture ensures high fidelity predictive modeling and hidden physics recovery for applications such as image reconstruction, pulse characterization and shaping, as well as hidden parameter discovery. The benefits of the FD-PINN for ultrafast nonlinear optics make it immediately experimentally deployable. FD-PINN represents the next generation of tools to study optical phenomena both through modeling and measurements for both forward and inverse problems.

In just the past few years multiple data-driven Artificial Intelligence Weather Prediction (AIWP) models have been developed, with new versions appearing almost monthly. Given this rapid development, the applicability of these models to operational forecasting has yet to be adequately explored and documented. To assess their utility for operational tropical cyclone (TC) forecasting, the NHC verification procedure is used to evaluate seven-day track and intensity predictions for northern hemisphere TCs from May-November 2023. Four open-source AIWP models are considered (FourCastNetv1, FourCastNetv2-small, GraphCast-operational and Pangu-Weather). The AIWP track forecast errors and detection rates are comparable to those from the best-performing operational forecast models. However, the AIWP intensity forecast errors are larger than those of even the simplest intensity forecasts based on climatology and persistence. The AIWP models almost always reduce the TC intensity, especially within the first 24 h of the forecast, resulting in a substantial low bias. The contribution of the AIWP models to the NHC model consensus was also evaluated. The consensus track errors are reduced by up to 11% at the longer time periods. The five-day NHC official track forecasts have improved by about 2% per year since 2001, so this represents more than a five-year gain in accuracy. Despite substantial negative intensity biases, the AIWP models have a neutral impact on the intensity consensus. These results show that the current formulation of the AIWP models have promise for operational TC track forecasts, but improved bias corrections or model reformulations will be needed for accurate intensity forecasts., Comment: 33 pages. 9 figures, 3 tables

Explainability in classification results are dependent upon the features used for classification. Data dependency graph features representing data movement are directly correlated with operational semantics, and subject to fine grained analysis. This study obtains accurate classification from the use of features tied to structure and semantics. By training an accurate model using labeled data, this feature representation of semantics is shown to be correlated with ground truth labels. This was performed using non-parametric learning with a novel feature representation on a large scale dataset, the Kaggle 2015 Malware dataset. The features used enable fine grained analysis, increase in resolution, and explainable inferences. This allows for the body of the term frequency distribution to be further analyzed and to provide an increase in feature resolution over term frequency features. This method obtains high accuracy from analysis of a single instruction, a method that can be repeated for additional instructions to obtain further increases in accuracy. This study evaluates the hypothesis that the semantic representation and analysis of structure are able to make accurate predications and are also correlated to ground truth labels. Additionally, similarity in the metric space can be calculated directly without prior training. Our results provide evidence that data dependency graphs accurately capture both semantic and structural information for increased explainability in classification results.

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

This paper explores the integration of cross-polarized stimulated Brillouin scattering (XP-SBS) with Kerr and quadratic nonlinearities in lithium niobate (LN) to enhance photonic device performance. Three novel applications are demonstrated: (i) a reconfigurable stimulated Brillouin laser (SBL) with 0.7-Hz narrow linewidth and 40-nm tunability, enabled by XP-SBS's thermo-optic phase matching; (ii) an efficient coherent mode converter achieving 55% conversion efficiency via intracavity Brillouin-enhanced four-wave mixing; (iii) a Brillouin-quadratic laser and frequency comb operational in near-infrared and visible bands, benefiting from the interaction between XP-SBS and quadratic nonlinearity. These advancements promise significant improvements in photonic technologies, including narrow-linewidth laser, microcomb generation, and optical signal processing, paving the way for more robust and versatile applications.

We combine graph neural networks (GNN) with an inexpensive and reliable structure generation approach based on the bond-valence method (BVM) to train accurate machine learning models for screening 222,960 halide perovskites using statistical estimates of the DFT/PBE formation energy (Ef), and the PBE and HSE band gaps (Eg). The GNNs were fined tuned using domain adaptation (DA) from a source model, which yields a factor of 1.8 times improvement in Ef and 1.2 - 1.35 times improvement in HSE Eg compared to direct training (i.e., without DA). Using these two ML models, 48 compounds were identified out of 222,960 candidates as both stable and that have an HSE Eg that is relevant for photovoltaic applications. For this subset, only 8 have been reported to date, indicating that 40 compounds remain unexplored to the best of our knowledge and therefore offer opportunities for potential experimental examination.

Networks of atom-centered coordination octahedra commonly occur in inorganic and hybrid solid-state materials. Characterizing their spatial arrangements and characteristics is crucial for relating structures to properties for many materials families. The traditional method using case-by-case inspection becomes prohibitive for discovering trends and similarities in large datasets. Here, we operationalize chemical intuition to automate the geometric parsing, quantification, and classification of coordination octahedral networks. We find axis-resolved tilting trends in ABO$_{3}$ perovskite polymorphs, which assist in detecting oxidation state changes. Moreover, we develop a scale-invariant encoding scheme to represent these networks, which, combined with human-assisted unsupervised machine learning, allows us to taxonomize the inorganic framework polytypes in hybrid iodoplumbates (A$_x$Pb$_y$I$_z$). Consequently, we uncover a violation of Pauling's third rule and the design principles underpinning their topological diversity. Our results offer a glimpse into the vast design space of atomic octahedral networks and inform high-throughput, targeted screening of specific structure types., Comment: 56 pages

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating., Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 71

The proposed polarized $^3$He$^{++}$ acceleration in RHIC and the future Electron-Ion Collider will require about $2\times10^{11}$ ions in the source pulse. A new technique had been proposed for production of high intensity polarized $^3$He$^{++}$ ion beams. It is based on ionization and accumulation of the $^3$He gas (polarized by metastability-exchange optical pumping and in the 5 T high magnetic field) in the existing Electron Beam Ion Source (EBIS). A novel $^3$He cryogenic purification and storage technique was developed to provide the required gas purity. An original gas refill and polarized $^3$He gas injection to the EBIS long drift tubes, (which serves as the storage cell) were developed to ensure polarization preservation. An infrared laser system for optical pumping and polarization measurements in the high 3--5 T field has been developed. The $^3$He polarization 80--85\% (and sufficiently long $\sim30$ min relaxation time) was obtained in the \lq\lq{open}\rq\rq\ cell configuration with refilling valve tube inlet and isolation valve closed. The development of the spin-rotator and $^3$He $^4$He absolute nuclear polarimeter at 6 MeV $^3$He$^{++}$ beam energy is also presented., Comment: 9 pages, 11 figures

PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we explain in detail these features and the overall design of the library. Code is available at https://www.github.com/KevinMusgrave/pytorch-adapt

Latent Semantic Analysis is a method of matrix decomposition used for discovering topics and topic weights in natural language documents. This study uses Latent Semantic Analysis to analyze the composition of binaries of malicious programs. The semantic representation of the term frequency vector representation yields a set of topics, each topic being a composition of terms. The vectors and topics were evaluated quantitatively using a spatial representation. This semantic analysis provides a more abstract representation of the program derived from its term frequency analysis. We use a metric space to represent a program as a collection of vectors, and a distance metric to evaluate their similarity within a topic. The segmentation of the vectors in this dataset provides increased resolution into the program structure., Comment: 11 pages, 5 figures

In this study we have presented a novel feature representation for malicious programs that can be used for malware classification. We have shown how to construct the features in a bottom-up approach, and analyzed the overlap of malicious and benign programs in terms of their components. We have shown that our method of analysis offers an increase in feature resolution that is descriptive of data movement in comparison to tf-idf features., Comment: arXiv admin note: text overlap with arXiv:2210.08034

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely adopted, but provide only a partial view of program semantics. This study is an empirical analysis of the networks composing malicious binaries in order to provide a complete representation of the structural properties of a program. This is accomplished by the measurement of structural properties of program networks in a malicious binary executable dataset. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. By providing an increase in feature resolution within labeled datasets of executable programs we provide a quantitative basis to interpret the results of classifiers trained on CFG graph features. An increase in feature resolution allows for the structural properties of program classes to be analyzed for patterns as well as their component parts. By capturing a complete picture of program graphs we can enable theoretical solutions for the mapping a program's operational semantics to its structure., Comment: 13 pages, 7 figures

Changes to hyperparameters can have a dramatic effect on model accuracy. Thus, the tuning of hyperparameters plays an important role in optimizing machine-learning models. An integral part of the hyperparameter-tuning process is the evaluation of model checkpoints, which is done through the use of "validators". In a supervised setting, these validators evaluate checkpoints by computing accuracy on a validation set that has labels. In contrast, in an unsupervised setting, the validation set has no such labels. Without any labels, it is impossible to compute accuracy, so validators must estimate accuracy instead. But what is the best approach to estimating accuracy? In this paper, we consider this question in the context of unsupervised domain adaptation (UDA). Specifically, we propose three new validators, and we compare and rank them against five other existing validators, on a large dataset of 1,000,000 checkpoints. Extensive experimental results show that two of our proposed validators achieve state-of-the-art performance in various settings. Finally, we find that in many cases, the state-of-the-art is obtained by a simple baseline method. To the best of our knowledge, this is the largest empirical study of UDA validators to date. Code is available at https://www.github.com/KevinMusgrave/powerful-benchmarker., Comment: This paper was previously titled Benchmarking Validation Methods for Unsupervised Domain Adaptation. This version contains new experiments, analysis, and figures

We report a measurement of two energy-weighted gamma cascade angular distributions from polarized slow neutron capture on the ${}^{35}$Cl nucleus, one parity-odd correlation proportional to $\vec{s_{n}} \cdot \vec{k_{\gamma}}$ and one parity-even correlation proportional to $\vec{s_{n}} \cdot \vec{k_{n}} \times \vec{k_{\gamma}}$. A parity violating asymmetry can appear in this reaction due to the weak nucleon-nucleon (NN) interaction which mixes opposite parity S and P-wave levels in the excited compound $^{36}$Cl nucleus formed upon slow neutron capture. If parity-violating (PV) and parity-conserving (PC) terms both exist, the measured differential cross section can be related to them via $\frac{d\sigma}{d\Omega}\propto1+A_{\gamma, PV}\cos\theta+A_{\gamma,PC}\sin\theta$. The PV and PC asymmetries for energy-weighted gamma cascade angular distributions for polarized slow neutron capture on $^{35}$Cl averaged over the neutron energies from 2.27~meV to 9.53~meV were measured to be $A_{\gamma,PV}=(-23.9\pm0.7)\times 10^{-6}$ and $A_{\gamma,PC}=(0.1\pm0.7)\times 10^{-6}$. These results are consistent with previous experimental results. Systematic errors were quantified and shown to be small compared to the statistical error. These asymmetries in the angular distributions of the gamma rays emitted from the capture of polarized neutrons in $^{35}$Cl were used to verify the operation and data analysis procedures for the NPDGamma experiment which measured the parity-odd asymmetry in the angular distribution of gammas from polarized slow neutron capture on protons.

Interest in unsupervised domain adaptation (UDA) has surged in recent years, resulting in a plethora of new algorithms. However, as is often the case in fast-moving fields, baseline algorithms are not tested to the extent that they should be. Furthermore, little attention has been paid to validation methods, i.e. the methods for estimating the accuracy of a model in the absence of target domain labels. This is despite the fact that validation methods are a crucial component of any UDA train/val pipeline. In this paper, we show via large-scale experimentation that 1) in the oracle setting, the difference in accuracy between UDA algorithms is smaller than previously thought, 2) state-of-the-art validation methods are not well-correlated with accuracy, and 3) differences between UDA algorithms are dwarfed by the drop in accuracy caused by validation methods.