With the emergence of graphene and other two-dimensional (2D) materials, transition metal dichalcogenides have been intensely investigated as potential 2D materials using experimental and theoretical methods. VSe2 is an especially interesting material since its bulk modification exhibits a charge density wave (CDW), the CDW is retained even for few-layer nanosheets, and monolayers of VSe2 are predicted to be ferromagnetic. In this work, we show that electron correlation has a profound effect on the magnetic properties and dynamic stability of VSe2 monolayers and bilayers. Including a Hubbard-U term in the DFT calculations strongly affects the magnetocrystalline anisotropy in the 1T-VSe2 structure, while leaving the 2H-polytype virtually unchanged. This demonstrates the importance of electronic correlations for the electrical and magnetic properties of 1T-VSe2. The Hubbard-U term changes the dynamic stability and the presence of imaginary modes of ferromagnetic 1T-VSe2 while affecting only the amplitudes in the non-magnetic phase. The Fermi surface of non-magnetic of 1T-VSe2 allows for nesting along the CDW vector, but plays no role in ferromagnetic 1T-VSe2. Following the eigenvectors of the soft modes in non-magnetic 1T-VSe2 monolayers yields a CDW structure with a 4×4 supercell and Peierls-type distortion in the atomic positions and electronic structure. The magnetic order indicates the potential for spin density wave structures.

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.

The composition and thickness of thin films determine their physical properties, making the ability to measure the number of atoms of different elements in films both technologically and scientifically important. For thin films, below a certain thickness, the X-ray fluorescence intensity of an element is proportional to the number of atoms. Converting this intensity to the number of atoms per unit area is challenging due to experimental geometries and other correction factors. Hence, the ratio of intensities is more commonly used to determine the composition in terms of element ratios using standards or a model. Here, the number of atoms per unit area was determined using X-ray structure information for over 20 different crystallographically aligned samples with integral unit cell thicknesses. The proportionality constant between intensity and the number of atoms per unit area was determined from linear fits of the background subtracted X-ray fluorescence intensity plotted versus the calculated number of atoms per unit area for each element. The results demonstrate that X-ray fluorescence is very sensitive, capable of measuring changes in the number of atoms of less than 1% of a monolayer for some elements in a variety of sample matrices. Using the calibrated values, an 8 unit cell thick MoSe2 was grown and characterized, demonstrating the usefulness of the ablity to quantify the number of atoms per unit area in a film.

(BiSe)(NbSe) heterostructures with n = 1-4 were synthesized using modulated elemental reactants. The BiSe bilayer structure changed from a rectangular basal plane with n = 1 to a square basal plane for n = 2-4. The BiSe in-plane structure was also influenced by small changes in the structure of the precursor, without significantly changing the out-of-plane diffraction pattern or value of the misfit parameter, δ. Density functional theory calculations on isolated BiSe bilayers showed that its lattice is very flexible, which may explain its readiness to adjust shape and size depending on the environment. Correlated with the changes in the BiSe basal plane structure, analysis of scanning transmission electron microscope images revealed that the occurrence of antiphase boundaries, found throughout the n = 1 compound, is dramatically reduced for the n = 2-4 compounds. X-ray photoelectron spectroscopy measurements showed that the Bi 5d, 5d doublet peaks narrowed toward higher binding energies as n increased from 1 to 2, also consistent with a reduction in the number of antiphase boundaries. Temperature-dependent electrical resistivity and Hall coefficient measurements of nominally stoichiometric samples in conjunction with structural refinements and XPS data suggest a constant amount of interlayer charge transfer independent of n. Constant interlayer charge transfer is surprising given the changes in the BiSe in-plane structure. The structural flexibility of the BiSe layer may be useful in designing multiple constituent heterostructures as an interlayer between structurally dissimilar constituents.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.