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We investigate structural coupling of the MnO6 octahedra across a film/substrate interface and the resultant changes of the physical properties of ultrathin La2/3Sr1/3MnO3 (LSMO) films. In order to isolate the effect of interfacial MnO6 octahedral behavior from that of epitaxial strain, LSMO films are grown on substrates with different symmetry and similar lattice parameters. Ultrathin LSMO films show an increased magnetization and electrical conductivity on cubic (LaAlO3)0.3(Sr2AlTaO6)0.7 (LSAT) compared to those grown on orthorhombic NdGaO3 (NGO) substrates, an effect that subsides as the thickness of the films is increased. This study demonstrates that interfacial structural coupling can play a critical role in the functional properties of oxide heterostructures.

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.

Magnetoelectric oxide heterostructures are proposed active layers for spintronic memory and logic devices, where information is conveyed through spin transport in the solid state. Incomplete theories of the coupling between local strain, charge, and magnetic order have limited their deployment into new information and communication technologies. In this study, we report direct, local measurements of strain- and charge-mediated magnetization changes in the La0.7Sr0.3MnO3/PbZr0.2Ti0.8O3 system using spatially resolved characterization techniques in both real and reciprocal space. Polarized neutron reflectometry reveals a graded magnetization that results from both local structural distortions and interfacial screening of bound surface charge from the adjacent ferroelectric. Density functional theory calculations support the experimental observation that strain locally suppresses the magnetization through a change in the Mn-eg orbital polarization. We suggest that this local coupling and magnetization suppression may be tuned by controlling the manganite and ferroelectric layer thicknesses, with direct implications for device applications.

Ferrimagnetic alloy thin films that exhibit perpendicular (out-of-plane) magnetic anisotropy (PMA) with low saturation magnetization, such as GdCo and Mn4N, were predicted to be favorable for hosting small Néel skyrmions for room temperature applications. Due to the exponential decay of interfacial Dzyaloshinskii-Moriya interaction (DMI) and the limited range of spin-orbit-torques, which can be used to drive skyrmion motion, the thickness of the ferrimagnetic layer has to be small, preferably under 20 nm. While there are examples of sub-20 nm, rare earth-transition metal (RE-TM), ferrimagnetic thin films fabricated by sputter deposition, to date rare-earth-free sub-20 nm Mn4N films with PMA have only been reported to be achieved by molecular beam epitaxy, which is not suitable for massive production. Here we report the successful thermal growth of sub-20 nm Mn4N films with PMA at 400-450 °C substrate temperatures on MgO substrates by reactive sputtering. The Mn4N films were achieved by reducing the surface roughness of MgO substrate through a high-temperature vacuum annealing process. The optimal films showed low saturation magnetization (Ms = 43 emu/cc), low magnetic anisotropy energy (0.7 Merg/cc), and a remanent magnetization to saturation magnetization ratio (Mr/Ms) near 1 at room temperature. Preliminary ab-initio density functional theory (DFT) calculations have confirmed the ferrimagnetic ground state of Mn4N grown on MgO. The magnetic properties, along with the high thermal stability of Mn4N thin films in comparison with RE-TM thin films, provide the platform for future studies of practical skyrmion-based spintronic materials.

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Growth of high quality two-dimensional transition metal dichalcogenide monolayers with the desired microstructure and morphology is critical for enabling key technological solutions. This is a non-trivial problem because the processing space is vast and lack of a priori guidelines impedes rapid progress. A machine learning approach is discussed that leverages the data present in published growth experiments to predict growth performance in regions of unexplored parameter space. Starting from the literature data on MoS2 thin films grown using chemical vapor deposition (CVD), a database is manually constructed. Unsupervised and supervised machine learning methods are used to learn from the compiled data by extracting trends that underlie the formation of MoS2 monolayers. Design rules are uncovered that establish the phase boundaries classifying monolayers from other possible outcomes, which offers future guidance of CVD experiments.

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