The virial theorem and exact properties of density functionals for periodic systems

In the framework of density functional theory, scaling and the virial theorem are essential tools for deriving exact properties of density functionals. Preexisting mathematical difficulties in deriving the virial theorem via scaling for periodic systems are resolved via a particular scaling technique. This methodology is employed to derive universal properties of the exchange-correlation energy functional for periodic systems.Comment: Accepted in PRB(R) 201

Exchange-correlation approximations for reduced-density-matrix-functional theory at finite temperature: Capturing magnetic phase transitions in the homogeneous electron gas

We derive an intrinsically temperature-dependent approximation to the correlation grand potential for many-electron systems in thermodynamical equilibrium in the context of finite-temperature reduced-density-matrix-functional theory (FT-RDMFT). We demonstrate its accuracy by calculating the magnetic phase diagram of the homogeneous electron gas. We compare it to known limits from highly accurate quantum Monte Carlo calculations as well as to phase diagrams obtained within existing exchange-correlation approximations from density functional theory and zero-temperature RDMFT

Effects of shading and covering material application for delaying harvest on gray mold disease severity

To delay the harvest of Sultani Cekirdeksiz grape variety and to reduce pre and post-harvest botrytis bunch rot severity, shading and covering material application were tested in 2009 to 2010 growing periods. In this study, grape vines were shaded with shading materials which had three different shading densities (35, 55, and 75% shading density) from veraison period to harvest. The grape vines were also covered with four different covering materials (transparent polyethylene, mogul, polypropen cross-stich and lifepack) before rainfall, at the end of August until harvest. The gray mold severity was recorded three times (before shading at unriped grape stage, veraison period, shortly after shading and twice at 20 day interval) during growing period. Based on the results of this study, the highest gray mold (Botrytis cinerea) severity was obtained in the control (uncovered and unshaded) treatment and the lowest disease severity was observed in lifepack treatment with or without shading. Since gray mold disease of grape was the main factor affecting harvest date of the crop lifepack, + 35 or 55% shading could be recommended to delay harvest and reduce the gray mold severity of grape in Manisa province-Turkey.Key words: Sultani seedless, table grape, shading, cover material, delaying harvest disease severity, Botrytis cinerea

Machine learning the electronic structure of matter across temperatures

We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser-heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions

Assessing the accuracy of hybrid exchange-correlation functionals for the density response of warm dense electrons

We assess the accuracy of common hybrid exchange-correlation (XC) functionals (PBE0, PBE0-1/3, HSE06, HSE03, and B3LYP) within Kohn-Sham density functional theory (KS-DFT) for the harmonically perturbed electron gas at parameters relevant for the challenging conditions of warm dense matter. Generated by laser-induced compression and heating in the laboratory, warm dense matter is a state of matter that also occurs in white dwarfs and planetary interiors. We consider both weak and strong degrees of density inhomogeneity induced by the external field at various wavenumbers. We perform an error analysis by comparing to exact quantum Monte-Carlo results. In the case of a weak perturbation, we report the static linear density response function and the static XC kernel at a metallic density for both the degenerate ground-state limit and for partial degeneracy at the electronic Fermi temperature. Overall, we observe an improvement in the density response for partial degeneracy when the PBE0, PBE0-1/3, HSE06, and HSE03 functionals are used compared to the previously reported results for the PBE, PBEsol, LDA, AM05, and SCAN functionals; B3LYP, on the other hand, does not perform well for the considered system. Together with the reduction of self-interaction errors, this seems to be the rationale behind the relative success of the HSE03 functional for the description of the experimental data on aluminum and liquid ammonia at WDM conditions

The Influence of Interspecific Competition and Host Preference on the Phylogeography of Two African Ixodid Tick Species

A comparative phylogeographic study on two economically important African tick species, Amblyomma hebraeum and Hyalomma rufipes was performed to test the influence of host specificity and host movement on dispersion. Pairwise AMOVA analyses of 277 mtDNA COI sequences supported significant population differentiation among the majority of sampling sites. The geographic mitochondrial structure was not supported by nuclear ITS-2 sequencing, probably attributed to a recent divergence. The three-host generalist, A. hebraeum, showed less mtDNA geographic structure, and a lower level of genetic diversity, while the more host-specific H. rufipes displayed higher levels of population differentiation and two distinct mtDNA assemblages (one predominantly confined to South Africa/Namibia and the other to Mozambique and East Africa). A zone of overlap is present in southern Mozambique. A mechanistic climate model suggests that climate alone cannot be responsible for the disruption in female gene flow. Our findings furthermore suggest that female gene dispersal of ticks is more dependent on the presence of juvenile hosts in the environment than on the ability of adult hosts to disperse across the landscape. Documented interspecific competition between the juvenile stages of H. rufipes and H. truncatum is implicated as a contributing factor towards disrupting gene flow between the two southern African H. rufipes genetic assemblages

Probing Iron in Earth's Core With Molecular-Spin Dynamics

Dynamic compression of iron to Earth-core conditions is one of the few ways to gather important elastic and transport properties needed to uncover key mechanisms surrounding the geodynamo effect. Herein a new machine-learned ab-initio derived molecular-spin dynamics (MSD) methodology with explicit treatment for longitudinal spin-fluctuations is utilized to probe the dynamic phase-diagram of iron. This framework uniquely enables an accurate resolution of the phase-transition kinetics and Earth-core elastic properties, as highlighted by compressional wave velocity and adiabatic bulk moduli measurements. In addition, a unique coupling of MSD with time-dependent density functional theory enables gauging electronic transport properties, critically important for resolving geodynamo dynamics.Comment: 3 Figures in main document, 8 Figures in the supplemental informatio

Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter

We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena in warm dense matter, but these potentials have often been calibrated for a narrow range of temperature and pressures. In contrast, we train a single ML-IAP over a wide range of temperatures, using density functional theory molecular dynamics (DFT-MD) data. Our approach overcomes computational limitations of DFT-MD simulations, enabling us to study transport and materials properties of matter at higher temperatures and longer time scales. We demonstrate the ML-IAP transferability across a wide range of temperatures using molecular-dynamics (MD) by examining the thermal conductivity, diffusion coefficient, viscosity, sound velocity, and ion-ion structure factor of aluminum up to about 60,000 K, where we find good agreement with previous theoretical data

Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful to the point of being recognized with a Nobel prize in 1998, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances science to frontiers intractable with any current solutions. This unprecedented modeling capability opens up an inexhaustible range of applications in astrophysics, novel materials discovery, and energy solutions for a sustainable future