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Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point in which steady state is reached, ensuring that figures-of-merits are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser., Comment: 10 pages, 7 figures

Social media has become integral to human interaction, providing a platform for communication and expression. However, the rise of hate speech on these platforms poses significant risks to individuals and communities. Detecting and addressing hate speech is particularly challenging in languages like Portuguese due to its rich vocabulary, complex grammar, and regional variations. To address this, we introduce TuPy-E, the largest annotated Portuguese corpus for hate speech detection. TuPy-E leverages an open-source approach, fostering collaboration within the research community. We conduct a detailed analysis using advanced techniques like BERT models, contributing to both academic understanding and practical applications, Comment: 15 pages, 7 figures, 10 tables

We study the robustness of the baryon acoustic oscillation (BAO) analysis to the underlying cosmological model. We focus on testing the standard BAO analysis that relies on the use of a template. These templates are constructed assuming a fixed fiducial cosmological model and used to extract the location of the acoustic peaks. Such "compressed analysis" had been shown to be unbiased when applied to the $\Lambda$CDM model and some of its extensions. However, it has not been known whether this type of analysis introduces biases in a wider range of cosmological models where the template may not fully capture relevant features in the BAO signal. In this study, we apply the compressed analysis to noiseless mock power spectra that are based on Horndeski models, a broad class of modified-gravity theories specified with eight additional free parameters. We study the precision and accuracy of the BAO peak-location extraction assuming DESI, DESI II, and MegaMapper survey specifications. We find that the bias in the extracted peak locations is negligible; for example, it is less than 10% of the statistical error for even the proposed future MegaMapper survey. Our findings indicate that the compressed BAO analysis is remarkably robust to the underlying cosmological model., Comment: 30 pages, 8 Figures, published version

Covalent Organic Frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry. These traits make COFs a highly promising choice for a range of applications. However, with their vast potential structures, exploring COFs experimentally can be challenging and time-consuming, yet it remains an attractive avenue for computational high-throughput studies. However, generating COF structures can be a time-consuming and challenging task. To address this challenge, here we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/lipelopesoliveira/pyCOFBuilder., Comment: 9 pages, 7 figures on the main text. 14 pages, 14 figures on supporting information

Redshift measurement has always been a constant need in modern astronomy and cosmology. And as new surveys have been providing an immense amount of data on astronomical objects, the need to process such data automatically proves to be increasingly necessary. In this article, we use simulated data from the Dark Energy Survey, and from a pipeline originally created to classify supernovae, we developed a linear regression algorithm optimized through novel automated machine learning (AutoML) frameworks achieving an error score better than ordinary data pre-processing methods when compared with other modern algorithms (such as XGBOOST). Numerically, the photometric prediction RMSE of type Ia supernovae events was reduced from 0.16 to 0.09 and the RMSE of all supernovae types decreased from 0.20 to 0.14. Our pipeline consists of four steps: through spectroscopic data points we interpolate the light curve using Gaussian process fitting algorithm, then using a wavelet transform we extract the most important features of such curves; in sequence we reduce the dimensionality of such features through principal component analysis, and in the end we applied super learning techniques (stacked ensemble methods) through an AutoML framework dedicated to optimize the parameters of several different machine learning models, better resolving the problem. As a final check, we obtained probability distribution functions (PDFs) using Gaussian kernel density estimations through the predictions of more than 50 models trained and optimized by AutoML. Those PDFs were calculated to replicate the original curves that used SALT2 model, a model used for the simulation of the raw data itself.

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture application. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO2 and N2 adsorption isotherms on 726 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), four partial charge schemes (no charge, Qeq, EQeq, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms., Comment: 18 pages, 6 figures, dataset available at 10.5281/zenodo.7106173

The mechanism(s) driving the early- and late-time accelerated expansion of the Universe represent one of the most compelling mysteries in fundamental physics today. The path to understanding the causes of early- and late-time acceleration depends on fully leveraging ongoing surveys, developing and demonstrating new technologies, and constructing and operating new instruments. This report presents a multi-faceted vision for the cosmic survey program in the 2030s and beyond that derives from these considerations. Cosmic surveys address a wide range of fundamental physics questions, and are thus a unique and powerful component of the HEP experimental portfolio., Comment: Submitted to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021); Topical Group Report for CF06 (Cosmic Frontier Topical Group on Dark Energy and Cosmic Acceleration: Complementarity of Probes and New Facilities

Cosmological analyses with type Ia supernovae (SNe Ia) often assume a single empirical relation between color and luminosity ($\beta$) and do not account for varying host-galaxy dust properties. However, from studies of dust in large samples of galaxies, it is known that dust attenuation can vary significantly. Here we take advantage of state-of-the-art modeling of galaxy properties to characterize dust parameters (dust attenuation $A_V$, and a parameter describing the dust law slope $R_V$) for the Dark Energy Survey (DES) SN Ia host galaxies using the publicly available \texttt{BAGPIPES} code. Utilizing optical and infrared data of the hosts alone, we find three key aspects of host dust that impact SN Ia cosmology: 1) there exists a large range ($\sim1-6$) of host $R_V$ 2) high stellar mass hosts have $R_V$ on average $\sim0.7$ lower than that of low-mass hosts 3) there is a significant ($>3\sigma$) correlation between the Hubble diagram residuals of red SNe Ia that when corrected for reduces scatter by $\sim13\%$ and the significance of the ``mass step'' to $\sim1\sigma$. These represent independent confirmations of recent predictions based on dust that attempted to explain the puzzling ``mass step'' and intrinsic scatter ($\sigma_{\rm int}$) in SN Ia analyses. We also find that red-sequence galaxies have both lower and more peaked dust law slope distributions on average in comparison to non red-sequence galaxies. We find that the SN Ia $\beta$ and $\sigma_{\rm int}$ both differ by $>3\sigma$ when determined separately for red-sequence galaxy and all other galaxy hosts. The agreement between fitted host-$R_V$ and SN Ia $\beta$ \& $\sigma_{\rm int}$ suggests that host dust properties play a major role in SN Ia color-luminosity standardization and supports the claim that SN Ia intrinsic scatter is driven by $R_V$ variation., Comment: 22 pages. Submitted to MNRAS

This invited Snowmass 2021 White Paper highlights the power of joint-analysis of astronomical transients in advancing HEP Science and presents research activities that can realize the opportunities that come with current and upcoming projects. Transients of interest include gravitational wave events, neutrino events, strongly-lensed quasars and supernovae, and Type~Ia supernovae specifically. These transients can serve as probes of cosmological distances in the Universe and as cosmic laboratories of extreme strong-gravity, high-energy physics. Joint analysis refers to work that requires significant coordination from multiple experiments or facilities so encompasses Multi-Messenger Astronomy and optical transient discovery and distributed follow-up programs., Comment: Minor updates to align with the feedback from the Snowmass Community Summer Study Workshop

A number of recent works have employed decision trees for the construction of explainable partitions that aim to minimize the $k$-means cost function. These works, however, largely ignore metrics related to the depths of the leaves in the resulting tree, which is perhaps surprising considering how the explainability of a decision tree depends on these depths. To fill this gap in the literature, we propose an efficient algorithm that takes into account these metrics. In experiments on 16 datasets, our algorithm yields better results than decision-tree clustering algorithms such as the ones presented in \cite{dasgupta2020explainable}, \cite{frost2020exkmc}, \cite{laber2021price} and \cite{DBLP:conf/icml/MakarychevS21}, typically achieving lower or equivalent costs with considerably shallower trees. We also show, through a simple adaptation of existing techniques, that the problem of building explainable partitions induced by binary trees for the $k$-means cost function does not admit an $(1+\epsilon)$-approximation in polynomial time unless $P=NP$, which justifies the quest for approximation algorithms and/or heuristics., Comment: 20 pages, 3 figures (7 subfigures), 7 tables. Includes a new section with experiments calibrating the trade-off between partition quality and explainability. Section 3 of the previous version, with theoretical results on the hardness of explainable $k$-means clustering, was removed and expanded into a separate article (arXiv:2208.09643)

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope's Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond., Comment: 32 pages, 18 figures, accepted for publication by ApJ

Despite the growing availability of high-capacity computational platforms, implementation complexity still has been a great concern for the real-world deployment of neural networks. This concern is not exclusively due to the huge costs of state-of-the-art network architectures, but also due to the recent push towards edge intelligence and the use of neural networks in embedded applications. In this context, network compression techniques have been gaining interest due to their ability for reducing deployment costs while keeping inference accuracy at satisfactory levels. The present paper is dedicated to the development of a novel compression scheme for neural networks. To this end, a new form of $\ell_0$-norm-based regularization is firstly developed, which is capable of inducing strong sparseness in the network during training. Then, targeting the smaller weights of the trained network with pruning techniques, smaller yet highly effective networks can be obtained. The proposed compression scheme also involves the use of $\ell_2$-norm regularization to avoid overfitting as well as fine tuning to improve the performance of the pruned network. Experimental results are presented aiming to show the effectiveness of the proposed scheme as well as to make comparisons with competing approaches., Comment: 34 pages, 9 figures, 7 tables

One of the primary sources of uncertainties in modeling the cosmic-shear power spectrum on small scales is the effect of baryonic physics. Accurate cosmology for Stage-IV surveys requires knowledge of the matter power spectrum deep in the nonlinear regime at the percent level. Therefore, it is important to develop reliable mitigation techniques to take into account baryonic uncertainties if information from small scales is to be considered in the cosmological analysis. In this work, we develop a new mitigation method for dealing with baryonic physics for the case of the shear angular power spectrum. The method is based on an extended covariance matrix that incorporates baryonic uncertainties informed by hydrodynamical simulations. We use the results from 13 hydrodynamical simulations and the residual errors arising from a fit to a $\Lambda$CDM model using the extended halo model code {\tt HMCode} to account for baryonic physics. These residual errors are used to model a so-called theoretical error covariance matrix that is added to the original covariance matrix. In order to assess the performance of the method, we use the 2D tomographic shear from four hydrodynamical simulations that have different extremes of baryonic parameters as mock data and run a likelihood analysis comparing the residual bias on $\Omega_m$ and $\sigma_8$ of our method and the HMCode for an LSST-like survey. We use different modelling of the theoretical error covariance matrix to test the robustness of the method. We show that it is possible to reduce the bias in the determination of the tested cosmological parameters at the price of a modest decrease in the precision., Comment: 10 pages, 5 figures

Scalable quantum technologies will require an unprecedented combination of precision and complexity for designing stable structures of well-controllable quantum systems. It is a challenging task to find a suitable elementary building block, of which a quantum network can be comprised in a scalable way. Here we present the working principle of such a basic unit, engineered using molecular chemistry, whose control and readout are executed using a nitrogen vacancy (NV) center in diamond. The basic unit we investigate is a synthetic polyproline with electron spins localized on attached molecular sidegroups separated by a few nanometers. We demonstrate the readout and coherent manipulation of very few ($\leq 6 $) of these $S=1/2$ electronic spin systems and access their direct dipolar coupling tensor. Our results show, that it is feasible to use spin-labeled peptides as a resource for a molecular-qubit based network, while at the same time providing simple optical readout of single quantum states through NV-magnetometry. This work lays the foundation for building arbitrary quantum networks using well-established chemistry methods, which has many applications ranging from mapping distances in single molecules to quantum information processing., Comment: Author name typo

In recent years, solid-state spin systems have emerged as promising candidates for quantum information processing (QIP). Prominent examples are the Nitrogen-Vacancy (NV) center in diamond, phosphorous dopants in silicon (Si:P), rare-earth ions in solids and V$_{\text{Si}}$-centers in Silicon-carbide (SiC). The Si:P system has demonstrated, that by eliminating the electron spin of the dopant, its nuclear spins can yield exceedingly long spin coherence times. For NV centers, however, a proper charge state for storage of nuclear spin qubit coherence has not been identified yet. Here, we identify and characterize the positively charged NV center as an electron-spin-less and optically inactive state by utilizing the nuclear spin qubit as a probe. We control the electronic charge and spin utilizing nanometer scale gate electrodes. We achieve a lengthening of the nuclear spin coherence times by a factor of 20. Surprisingly, the new charge state allows switching the optical response of single nodes facilitating full individual addressability., Comment: 8 pages, 4 figures, 1 table

Atomic-size spin defects in solids are unique quantum systems. Most applications require nanometer positioning accuracy, which is typically achieved by low energy ion implantation. So far, a drawback of this technique is the significant residual implantation-induced damage to the lattice, which strongly degrades the performance of spins in quantum applications. In this letter we show that the charge state of implantation-induced defects drastically influences the formation of lattice defects during thermal annealing. We demonstrate that charging of vacancies localized at e.g. individual nitrogen implantation sites suppresses the formation of vacancy complexes, resulting in a tenfold-improved spin coherence time of single nitrogen-vacancy (NV) centers in diamond. This has been achieved by confining implantation defects into the space charge layer of free carriers generated by a nanometer-thin boron-doped diamond structure. Besides, a twofold-improved yield of formation of NV centers is observed. By combining these results with numerical calculations, we arrive at a quantitative understanding of the formation and dynamics of the implanted spin defects. The presented results pave the way for improved engineering of diamond spin defect quantum devices and other solid-state quantum systems.

Magnetic sensing and imaging instruments are important tools in biological and material sciences. There is an increasing demand for attaining higher sensitivity and spatial resolution, with implementations using a single qubit offering potential improvements in both directions. In this article we describe a scanning magnetometer based on the nitrogen-vacancy center in diamond as the sensor. By means of a quantum-assisted readout scheme together with advances in photon collection efficiency, our device exhibits an enhancement in signal to noise ratio of close to an order of magnitude compared to the standard fluorescence readout of the nitrogen-vacancy center. This is demonstrated by comparing non-assisted and assisted methods in a $T_1$ relaxation time measurement., Comment: Accepted version

Coherently coupled pairs or multimers of nitrogen-vacancy defect electron spins in diamond have many promising applications especially in quantum information processing (QIP) but also in nanoscale sensing applications. Scalable registers of spin qubits are essential to the progress of QIP. Ion implantation is the only known technique able to produce defect pairs close enough to allow spin coupling via dipolar interaction. Although several competing methods have been proposed to increase the resulting resolution of ion implantation, the reliable creation of working registers is still to be demonstrated. The current limitation are residual radiation-induced defects, resulting in degraded qubit performance as trade-off for positioning accuracy. Here we present an optimized estimation of nanomask implantation parameters that are most likely to produce interacting qubits under standard conditions. We apply our findings to a well-established technique, namely masks written in electron-beam lithography, to create coupled defect pairs with a reasonable probability. Furthermore, we investigate the scaling behavior and necessary improvements to efficiently engineer interacting spin architectures., Comment: Main article and supplementary information in one document. 20 pages, 11 figures

Newly discovered van der Waals materials like MoS$_2$, WSe$_2$, hexagonal boron nitride (h-BN), and recently $\mathrm{C}_2\mathrm{N}$ have sparked intensive research to unveil the quantum behavior associated with their 2D structure. Of great interest are 2D materials that host single quantum emitters. h-BN, with a band gap of 5.95 eV, has been shown to host single quantum emitters which are stable at room temperature in the UV and visible spectral range. In this paper we investigate correlations between h-BN structural features and emitter location from bulk down to the monolayer at room temperature. We demonstrate that chemical etching and ion irradiation can generate emitters in h-BN. We analyze the emitters' spectral features and show that they are dominated by the interaction of their electronic transition with a single Raman active mode of h-BN. Photodynamics analysis reveals diverse rates between the electronic states of the emitter. The emitters show excellent photo stability even under ambient conditions and in monolayers. Comparing the excitation polarization between different emitters unveils a connection between defect orientation and the h-BN hexagonal structure. The sharp spectral features, color diversity, room-temperature stability, long-lived metastable states, ease of fabrication, proximity of the emitters to the environment, outstanding chemical stability, and biocompatibility of h-BN provide a completely new class of systems that can be used for sensing and quantum photonics applications., Comment: In Nano Letters, ACS, 2016. Title change. Revision according to peer-review report. SI can be downloaded from http://pubs.acs.org/doi/suppl/10.1021/acs.nanolett.6b03268/suppl_file/nl6b03268_si_001.pdf

Hybrid quantum systems (HQSs) have attracted several research interests in the last years. In this Letter, we report on the design, fabrication, and characterization of a novel diamond architecture for HQSs that consists of a high quality thin circular diamond membrane with embedded near-surface nitrogen-vacancy centers (NVCs). To demonstrate this architecture, we employed the NVCs by means of their optical and spin interfaces as nanosensors of the motion of the membrane under static pressure and in-resonance vibration, as well as the residual stress of the membrane. Driving the membrane at its fundamental resonance mode, we observed coupling of this vibrational mode to the spin of the NVCs by Hahn echo signal. Our realization of this architecture will enable futuristic HQS-based applications in diamond piezometry and vibrometry, as well as spin-mechanical and mechanically mediated spin-spin coupling in quantum information science., Comment: 6 Pages, 4 Figures

Near-surface nitrogen-vacancy ({NV}) centers in diamond have been successfully employed as atomic-sized magnetic field sensors for external spins over the last years. A key challenge is still to develop a method to bring NV centers at nanometer proximity to the diamond surface while preserving their optical and spin properties. To that aim we present a method of controlled diamond etching with nanometric precision using an oxygen inductively coupled plasma (ICP) process. Importantly, no traces of plasma-induced damages to the etched surface could be detected by X-ray photoelectron spectroscopy (XPS) and confocal photoluminescence microscopy techniques. In addition, by profiling the depth of NV centers created by 5.0 keV of nitrogen implantation energy, no plasma-induced quenching in their fluorescence could be observed. Moreover, the developed etching process allowed even the channeling tail in their depth distribution to be resolved. Furthermore, treating a 12C isotopically purified diamond revealed a threefold increase in T2 times for NV centers with <4 nm of depth (measured by NMR signal from protons at the diamond surface) in comparison to the initial oxygen-terminated surface., Comment: 5 pages, 4 figures