A self-consistent system of additive covalent radii, R(AB)=r(A) + r(B), is set up for the entire periodic table, Groups 1-18, Z=1-118. The primary bond lengths, R, are taken from experimental or theoretical data corresponding to chosen group valencies. All r(E) values are obtained from the same fit. Both E-E, E-H, and E-CH 3 data are incorporated for most elements, E. Many E-E' data inside the same group are included. For the late main groups, the system is close to that of Pauling. For other elements it is close to the methyl-based one of Suresh and Koga [J. Phys. Chem. A 2001, 105, 5940] and its predecessors. For the diatomic alkalis MM' and halides XX', separate fits give a very high accuracy. These primary data are then absorbed with the rest. The most notable exclusion are the transition-metal halides and chalcogenides which are regarded as partial multiple bonds. Other anomalies include H 2 and F 2 . The standard deviation for the 410 included data points is 2.8 pm.

Molecular single-bond covalent radii for elements 1-118.

Tremendous advances have been made in nickel catalysis over the past decade Several key properties of nickel, such as facile oxidative addition and ready access to multiple oxidation states, have allowed the development of a broad range of innovative reactions In recent years, these properties have been increasingly understood and used to perform transformations long considered exceptionally challenging Here we discuss some of the most recent and significant developments in homogeneous nickel catalysis, with an emphasis on both synthetic outcome and mechanism

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Recent advances in homogeneous nickel catalysis

The response of the worldwide scientific community to the discovery in 2008 of superconductivity at T c = 26 K in the Fe-based compound LaFeAsO1−x F x has been very enthusiastic. In short order, ot...

The puzzle of high temperature superconductivity in layered iron pnictides and chalcogenides

Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozen 2D materials have been successfully synthesized or exfoliated. Here, we search for 2D materials that can be easily exfoliated from their parent compounds. Starting from 108,423 unique, experimentally known 3D compounds, we identify a subset of 5,619 compounds that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van der Waals density functional theory, validated against experimental structural data and calculated random phase approximation binding energies, further allowed the identification of 1,825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1,036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds, we explore vibrational, electronic, magnetic and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors.

Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

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Recent advances and applications of machine learning in solid-state materials science

A new set of covalent atomic radii has been deduced from crystallographic data for most of the elements with atomic numbers up to 96. The proposed radii show a well behaved periodic dependence that allows us to interpolate a few radii for elements for which structural data is lacking, notably the noble gases. The proposed set of radii therefore fills most of the gaps and solves some inconsistencies in currently used covalent radii. The transition metal and lanthanide contractions as well as the differences in covalent atomic radii between low spin and high spin configurations in transition metals are illustrated by the proposed radii set.

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Covalent radii revisited

A new family of functionalized ligands derived from norborn-5-ene-2,3-dicarboxylic anhydride has been used in Suzuki C–C cross-couplings between aryl boronic acids and aryl bromide derivatives in [BMI][PF6] (BMI = 1-n-butyl-3-methyl-imidazolium), using palladium acetate as catalytic precursor. High conversions and yields are obtained when amine ligands containing hydroxy groups are involved. TEM analyses after catalysis show the formation of small nanoparticles, in contrast to the agglomerates observed when nanoparticles are intentionally preformed, with a consequent decrease in the catalytic activity in the latter case. Some tests, including the correlation effect between solvent and ligand, are carried out to try to identify the true nature of the catalyst. All the results obtained suggest that formation of nanoparticles is required to lead to a catalytically active system.

Palladium catalyzed Suzuki C-C couplings in an ionic liquid: nanoparticles responsible for the catalytic activity.

Control of the dimensionality and nuclearity in pyraziloximatocopper(II) chemistry has been achieved by tuning of the donor properties of the counteranions.

Anionic tuning of the dimensionality in copper oximato chemistry.

Three new manganese compounds have been obtained by reaction of the 2,6-diacetylpyridine dioxime (dapdoH2) ligand with several MnII salts. By using a variety of manganese(II) carboxylates, three MnII derivatives with general formula [Mn3(RCOO)6(dapdoH2)2], where RCOO– = formate (1), acetate (2), and benzoate (3), were obtained. Structural analysis reveals chains of trinuclear units for 1 and isolated trinuclear molecules for 2. Compound 1 provides an example of a new topology for the [Mn3(RCOO)6(L)2] general formula. Magnetic susceptibility studies reveal topological ferrimagnetism for 1 and conventional antiferromagnetic response for 2 and 3, with coupling constants of –3.1, –3.2, and –1.4 cm–1 inside the trimeric units of 1, 2, and 3, respectively.(© Wiley-VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2008)

Manganese(II) Trimeric Systems Derived from Pyridyldioximato Ligands: Synthesis, Crystal Structure, and Magnetic Characterization

Assembly of triangular {MnIII3(O)(salox)3}+ fragments mediated by azido ligands, results in the dodecanuclear [Mn12O4(salox)12(N3)4(MeOH)4(H2O)2] complex with S = 8 ground state and SMM response.

Beatriz Cordero

Papers

Covalent radii revisited

Palladium catalyzed Suzuki C-C couplings in an ionic liquid: nanoparticles responsible for the catalytic activity.

Anionic tuning of the dimensionality in copper oximato chemistry.

Manganese(II) Trimeric Systems Derived from Pyridyldioximato Ligands: Synthesis, Crystal Structure, and Magnetic Characterization

Building of a novel Mn12 Single Molecule Magnet by assembly of anisotropic {Mn3(μ3-O)(salox)3}+ triangles