Distinguishing reionization models using the largest cluster statistics of the 21-cm maps

The evolution of topology and morphology of ionized or neutral hydrogen during different stages of the Epoch of Reionization (EoR) have the potential to provide us a great amount of information about the properties of the ionizing sources during this era. We compare a variety of reionization source models in terms of the geometrical properties of the ionized regions. We show that the percolation transition in the ionized hydrogen, as studied by tracing the evolution of the Largest Cluster Statistics (LCS), is a robust statistic that can distinguish the fundamentally different scenarios -- inside-out and outside-in reionization. Particularly, the global neutral fraction at the onset of percolation is significantly higher for the inside-out scenario as compared to that for the outside-in reionization. In complementary to percolation analysis, we explore the shape and morphology of the ionized regions as they evolve in different reionization models in terms of the Shapefinders (SFs) that are ratios of the Minkowski functionals (MFs). The shape distribution can readily discern the reionization scenario with extreme non-uniform recombination in the IGM, such as the clumping model. In the rest of the reionization models, the largest ionized region abruptly grows only in terms of its third SF - 'length' - during percolation while the first two SFs - 'thickness' and 'breadth' - remain stable. Thus the ionized hydrogen in these scenarios becomes highly filamentary near percolation and exhibit a 'characteristic cross-section' that varies among the source models. Therefore, the geometrical studies based on SFs, together with the percolation analysis can shed light on the reionization sources.Comment: 33 pages, 16 figures, accepted for publication in JCA

Interpreting the HI 21-cm cosmology maps through Largest Cluster Statistics -- I: Impact of the synthetic SKA1-Low observations

We analyse the evolution of the largest ionized region using the topological and morphological evolution of the redshifted 21-cm signal coming from the neutral hydrogen distribution during the different stages of reionization. For this analysis, we use the "Largest Cluster Statistics" - LCS. We mainly study the impact of the array synthesized beam on the LCS analysis of the 21-cm signal considering the upcoming low-frequency Square Kilometer Array (SKA1-Low) observations using a realistic simulation for such observation based on the 21cmE2E-pipeline using OSKAR. We find that bias in LCS estimation is introduced in synthetic observations due to the array beam. This in turn shifts the apparent percolation transition point towards the later stages of reionization. The biased estimates of LCS, occurring due to the effect of the lower resolution (lack of longer baselines) and the telescope synthesized beam will lead to a biased interpretation of the reionization history. This is important to note while interpreting any future 21-cm signal images from upcoming or future telescopes like the SKA, HERA, etc. We conclude that one may need denser $uv$-coverage at longer baselines for a better deconvolution of the array synthesized beam from the 21-cm images and a relatively unbiased estimate of LCS from such images.Comment: 37 pages, 14 figures, text revised, Comments are welcom

Electrical conductivity during incipient melting in the oceanic low-velocity zone

International audienceThe low-viscosity layer in the upper mantle, the asthenosphere, is a requirement for plate tectonics1. The seismic low velocities and the high electrical conductivities of the asthenosphere are attributed either to subsolidus, water-related defects in olivine minerals2, 3, 4 or to a few volume per cent of partial melt5, 6, 7, 8, but these two interpretations have two shortcomings. First, the amount of water stored in olivine is not expected to be higher than 50 parts per million owing to partitioning with other mantle phases9 (including pargasite amphibole at moderate temperatures10) and partial melting at high temperatures9. Second, elevated melt volume fractions are impeded by the temperatures prevailing in the asthenosphere, which are too low, and by the melt mobility, which is high and can lead to gravitational segregation11, 12. Here we determine the electrical conductivity of carbon-dioxide-rich and water-rich melts, typically produced at the onset of mantle melting. Electrical conductivity increases modestly with moderate amounts of water and carbon dioxide, but it increases drastically once the carbon dioxide content exceeds six weight per cent in the melt. Incipient melts, long-expected to prevail in the asthenosphere10, 13, 14, 15, can therefore produce high electrical conductivities there. Taking into account variable degrees of depletion of the mantle in water and carbon dioxide, and their effect on the petrology of incipient melting, we calculated conductivity profiles across the asthenosphere for various tectonic plate ages. Several electrical discontinuities are predicted and match geophysical observations in a consistent petrological and geochemical framework. In moderately aged plates (more than five million years old), incipient melts probably trigger both the seismic low velocities and the high electrical conductivities in the upper part of the asthenosphere, whereas in young plates4, where seamount volcanism occurs6, a higher degree of melting is expected

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

Eliminating imaginary vibrational frequencies in quantum-chemical cluster models of enzymatic active sites

In constructing finite models of enzyme active sites for use in quantum-chemical calculations, atoms at the periphery of the model system are often constrained to prevent structural collapse during geometry relaxation. A simple fixed-atom or ``coordinate lock\u27\u27 approach is commonly employed but leads to undesirable artifacts including the appearance of small imaginary frequencies. These preclude the evaluation of finite-temperature free energy corrections, limiting thermochemical calculations to enthalpies only. Full-dimensional vibrational frequency calculations are possible by replacing the fixed-atom constraints with harmonic confining potentials, and here we compare that approach to an alternative strategy in which fixed-atom contributions to the Hessian are simply omitted. While that approach does eliminate imaginary frequencies, it tends to underestimate both the zero-point energy and the vibrational entropy, in addition to the artificial rigidity already introduced by fixed-atom constraints. Harmonic confining potentials eliminate imaginary frequencies and provide a flexible means to construct models that can be used in unconstrained geometry relaxations

Density Functional Theory of Water with the Machine-Learned DM21 Functional

The delicate interplay between functional-driven and density-driven errors in density functional theory (DFT) has hindered traditional density functional approximations (DFAs) from providing an accurate description of water for over 30 years. Recently, the deep-learned DeepMind 21 (DM21) functional has been shown to overcome the limitations of traditional DFAs as it is free of delocalization error. To determine if DM21 can enable a molecular-level description of the physical properties of aqueous systems within Kohn-Sham DFT, we assess the accuracy of the DM21 functional for neutral, protonated, and deprotonated water clusters. We find that the ability of DM21 to accurately predict the energetics of aqueous clusters varies significantly with cluster size. Additionally, we introduce the many-body MB-DM21 potential derived from DM21 data within the many-body expansion of the energy and use it in simulations of liquid water as a function of temperature at ambient pressure. We find that size-dependent functional-driven errors identified in the analysis of the energetics of small clusters calculated with the DM21 functional result in the MB-DM21 potential systematically overestimating the hydrogen-bond strength and, consequently, predicting a more ice-like local structure of water at room temperature

Balance between physical interpretability and energetic predictability in widely used dispersion-corrected density functionals

We assess the performance of different dispersion models for several popular density functionals across a diverse set of non-covalent systems, ranging from the benzene dimer to molecular crystals. By analyzing the interaction energies and their individual components, we demonstrate that there exists variability across different systems for empirical dispersion models, which are calibrated for reproducing the interaction energies of specific systems. Thus, parameter fitting may undermine the underlying physics, as dispersion models rely on error compensation among the different components of the interaction energy. Energy decomposition analyses reveal that, the accuracy of revPBE-D3 for some aqueous systems originates from significant compensation between dispersion and charge transfer energies. However, revPBE-D3 is less accurate in describing systems where error compensation is incomplete, such as the benzene dimer. Such cases highlight the propensity for unpredictable behavior in various dispersion-corrected density functionals across a wide range of molecular systems, akin to the behavior of force fields. On the other hand, we find that SCAN-rVV10, a targeted-dispersion approach, affords significant reductions in errors associated with the lattice energies of molecular crystals, whilst it has limited accuracy in reproducing structural properties. Given the ubiquitous nature of non-covalent interactions and the key role of density functional theory in computational sciences, the future development of dispersion models should prioritize the faithful description of the dispersion energy, a shift that promises greater accuracy in capturing the underlying physics across diverse molecular and extended systems

Elevating Density Functional Theory to Chemical Accuracy for Water Simulations through a Density-Corrected Many-Body Formalism

Density functional theory (DFT) has been extensively used to model the properties of water. Albeit maintaining a good balance between accuracy and efficiency, no density functional has so far achieved the degree of accuracy necessary to correctly predict the properties of water across the entire phase diagram. Here, we present density-corrected SCAN (DC-SCAN) calculations for water which, minimizing density-driven errors, elevate the accuracy of the SCAN functional to that of “gold standard” coupled-cluster theory. Building upon the accuracy of DC-SCAN within a many-body formalism, we introduce a data-driven many-body potential energy function, MB-SCAN(DC), that quantitatively reproduces coupled cluster reference values for interaction, binding, and individual many-body energies of water clusters. Importantly, molecular dynamics simulations carried out with MB-SCAN(DC) also reproduce the properties of liquid water, which thus demonstrates that MB-SCAN(DC) is effectively the first DFT-based model that correctly describes water from the gas to the liquid phase

Data-Driven Many-Body Potentials from Density Functional Theory for Aqueous Phase Chemistry

Density functional theory (DFT) has been applied to modeling molecular interactions in water for over three decades. The ubiquity of water in chemical and biological processes demands a unified understanding of its physics, from the single-molecule to the thermodynamic limit and everything in between. Recent advances in the development of data-driven and machine-learning potentials have accelerated simulation of water and aqueous systems with DFT accuracy. However, the anomalous properties of water in the condensed phase, where a rigorous treatment of both local and non-local many-body interactions is in order, is often unsatisfactory or partially missing in DFT models of water. In this review, we discuss the modeling of water and aqueous systems based on DFT, and provide a comprehensive description of a general theoretical/computational framework for the development of data-driven many-body potentials from DFT reference data. This framework, coined MB-DFT, readily enables efficient many-body molecular dynamics (MB-MD) simulations of small molecules, in both gas and condensed phases, while preserving the accuracy of the underly- ing DFT model. Theoretical considerations are emphasized, including the role that the delocalization error plays in MB-DFT potentials of water, and the possibility to elevate DFT and MB-DFT to near-chemical-accuracy through a density-corrected formalism. The development of the MB-DFT framework is described in detail, along with its application in MB-MD simulations and recent extension to the modeling of reactive processes in solution within a quantum mechanics/many-body molecular mechanics (QM/MB-MM) scheme, using water as a prototypical solvent. Finally, we identify open challenges and discuss future directions for MB-DFT and QM/MB-MM simulations in condensed phases

Elevating density functional theory to chemical accuracy for water simulations through a density-corrected many-body formalism.

Density functional theory (DFT) has been extensively used to model the properties of water. Albeit maintaining a good balance between accuracy and efficiency, no density functional has so far achieved the degree of accuracy necessary to correctly predict the properties of water across the entire phase diagram. Here, we present density-corrected SCAN (DC-SCAN) calculations for water which, minimizing density-driven errors, elevate the accuracy of the SCAN functional to that of "gold standard" coupled-cluster theory. Building upon the accuracy of DC-SCAN within a many-body formalism, we introduce a data-driven many-body potential energy function, MB-SCAN(DC), that quantitatively reproduces coupled cluster reference values for interaction, binding, and individual many-body energies of water clusters. Importantly, molecular dynamics simulations carried out with MB-SCAN(DC) also reproduce the properties of liquid water, which thus demonstrates that MB-SCAN(DC) is effectively the first DFT-based model that correctly describes water from the gas to the liquid phase