A unified electrostatic and cavitation model for first-principles molecular dynamics in solution

The electrostatic continuum solvent model developed by Fattebert and Gygi is combined with a first-principles formulation of the cavitation energy based on a natural quantum-mechanical definition for the surface of a solute. Despite its simplicity, the cavitation contribution calculated by this approach is found to be in remarkable agreement with that obtained by more complex algorithms relying on a large set of parameters. Our model allows for very efficient Car-Parrinello simulations of finite or extended systems in solution, and demonstrates a level of accuracy as good as that of established quantum-chemistry continuum solvent methods. We apply this approach to the study of tetracyanoethylene dimers in dichloromethane, providing valuable structural and dynamical insights on the dimerization phenomenon

A self-consistent perturbative evaluation of ground state energies: application to cohesive energies of spin lattices

The work presents a simple formalism which proposes an estimate of the ground state energy from a single reference function. It is based on a perturbative expansion but leads to non linear coupled equations. It can be viewed as well as a modified coupled cluster formulation. Applied to a series of spin lattices governed by model Hamiltonians the method leads to simple analytic solutions. The so-calculated cohesive energies are surprisingly accurate. Two examples illustrate its applicability to locate phase transition.Comment: Accepted by Phys. Rev.

Machine learning for regional crop yield forecasting in Europe

Crop yield forecasting at national level relies on predictors aggregated from smaller spatial units to larger ones according to harvested crop areas. Such crop areas come from land cover maps or reported statistics, both of which can have errors and uncertainties. Sub-national or regional crop yield forecasting minimizes the propagation of these errors to some extent. In addition, regional forecasts provide added value and insights to stakeholders on regional differences within a country, which would otherwise compensate each other at national level. We propose a crop yield forecasting approach for multiple spatial levels based on regional crop yield forecasts from machine learning. Machine learning, with its data-driven approach, can leverage larger data sizes and capture nonlinear relationships between predictors and yield at regional level. We designed a generic machine learning workflow to demonstrate the benefits of regional crop yield forecasting in Europe. To evaluate the quality and usefulness of regional forecasts, we predicted crop yields for 35 case studies, including nine countries that are major producers of six crops (soft wheat, spring barley, sunflower, grain maize, sugar beets and potatoes). Machine learning models at regional level had lower normalized root mean squared errors (NRMSE) and uncertainty than a linear trend model, with Wilcoxon p-values of 3e-7 and 2e-7 for 60 days before harvest and end of season respectively. Similarly, regional machine learning forecasts aggregated to national level had lower NRMSEs than forecasts from an operational system in 18 out of 35 cases 60 days before harvest, with a Wilcoxon p-value of 0.95 indicating similar performance. Our models have room for improvement, especially during extreme years. Nevertheless, regional crop yield forecasts from machine learning and aggregated national forecasts provide a consistent forecasting method across spatial levels and insights from regional differences to support important policy decisions

Metagenomic survey of the microbiome of ancient Siberian permafrost and modern Kamchatkan cryosols

In the context of global warming, the melting of arctic permafrost raises the threat of a re-emergence of microorganisms some of which were shown to remain viable in ancient frozen soils for up to half a million years. In order to evaluate this risk, it is of interest to acquire a better knowledge of the composition of the microbial communities found in this understudied environment. Here we present a metagenomics analysis of 12 soil samples from Russian Arctic and subarctic pristine areas: Chukotka, Yakutia, and Kamchatka, including 9 permafrost samples collected at various depths. These large datasets (9.2 1011 total bp) were assembled (525,313 contigs > 5kb), their encoded protein contents predicted, then used to perform taxonomical assignments of bacterial, archaeal, and eukaryotic organisms, as well as DNA viruses. The various samples exhibited variable DNA contents and highly diverse taxonomic profiles showing no obvious relationship with their locations, depths or deposit ages. Bacteria represented the largely dominant DNA fraction (95%) in all samples, followed by archaea (3.2%), surprisingly little eukaryotes (0.5%), and viruses (0.4%). Although no common taxonomic pattern was identified, the samples shared unexpected high frequencies of β-lactamase genes, almost 0.9 copy/bacterial genome. In addition of known environmental threats, the particularly intense warming of the Arctic might thus enhance the spread of bacterial antibiotic resistances, today's major challenge in public health. β-lactamases were also observed at high frequency in other types of soils, suggesting their general role in the regulation of bacterial populations

Low energy measurement of the 7Be(p,gamma)8B cross section

We have measured the cross section of the 7Be(p,gamma)8B reaction for E_cm = 185.8 keV, 134.7 keV and 111.7 keV using a radioactive 7Be target (132 mCi). Single and coincidence spectra of beta^+ and alpha particles from 8B and 8Be^* decay, respectively, were measured using a large acceptance spectrometer. The zero energy S factor inferred from these data is 18.5 +/- 2.4 eV b and a weighted mean value of 18.8 +/- 1.7 eV b (theoretical uncertainty included) is deduced when combining this value with our previous results at higher energies.Comment: Accepted for publication in Phys. Rev. Let

The role of dynamical polarization of the ligand to metal charge transfer excitations in {\em ab initio} determination of effective exchange parameters

The role of the bridging ligand on the effective Heisenberg coupling parameters is analyzed in detail. This analysis strongly suggests that the ligand-to-metal charge transfer excitations are responsible for a large part of the final value of the magnetic coupling constant. This permits to suggest a new variant of the Difference Dedicated Configuration Interaction (DDCI) method, presently one of the most accurate and reliable for the evaluation of magnetic effective interactions. This new method treats the bridging ligand orbitals mediating the interaction at the same level than the magnetic orbitals and preserves the high quality of the DDCI results while being much less computationally demanding. The numerical accuracy of the new approach is illustrated on various systems with one or two magnetic electrons per magnetic center. The fact that accurate results can be obtained using a rather reduced configuration interaction space opens the possibility to study more complex systems with many magnetic centers and/or many electrons per center.Comment: 7 pages, 4 figure

Equilibrium between radiation and matter for classical relativistic multiperiodic systems. II. Study of radiative equilibrium with Rayleigh-Jeans radiation

We continue the study of the problem of equilibrium between radiation and classical relativistic systems begun previously Phys. Rev. D 27 1254 (1983). We consider the emission and absorption of energy by a relativistic pointlike particle immersed in a Rayleigh-Jeans radiation field. The particle is acted upon by a force which, if alone, would produce a multiply periodic motion. It is shown that radiative balance at each frequency holds. A discussion is given of the results reported in both papers

Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser

Citation: Ekeberg, T., Svenda, M., Abergel, C., Maia, F., Seltzer, V., Claverie, J. M., . . . Hajdu, J. (2015). Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser. Physical Review Letters, 114(9), 6. doi:10.1103/PhysRevLett.114.098102We present a proof-of-concept three-dimensional reconstruction of the giant mimivirus particle from experimentally measured diffraction patterns from an x-ray free-electron laser. Three-dimensional imaging requires the assembly of many two-dimensional patterns into an internally consistent Fourier volume. Since each particle is randomly oriented when exposed to the x-ray pulse, relative orientations have to be retrieved from the diffraction data alone. We achieve this with a modified version of the expand, maximize and compress algorithm and validate our result using new methods.Additional Authors: Andersson, I.;Loh, N. D.;Martin, A. V.;Chapman, H.;Bostedt, C.;Bozek, J. D.;Ferguson, K. R.;Krzywinski, J.;Epp, S. W.;Rolles, D.;Rudenko, A.;Hartmann, R.;Kimmel, N.;Hajdu, J