Electrical and magnetic properties of the complete solid solution series between SrRuO3 and LaRhO3: Filling t2g versus tilting

A complete solid solution series between the t2g^4 perovskite ferromagnet SrRuO3 and the diamagnetic t2g^6 perovskite LaRhO3 has been prepared. The evolution with composition x in (SrRuO3)(1-x)(LaRhO3)(x) of the crystal structure and electrical and magnetic properties has been studied and is reported here. As x increases, the octahedral tilt angle gradually increases, along with the pseudocubic lattice parameter and unit cell volume. Electrical resistivity measurements reveal a compositionally driven metal to insulator transition between x = 0.1 and 0.2. Ferromagnetic ordering gives over to glassy magnetism for x > 0.3 and no magnetic ordering is found above 2 K for x > 0.5. M_sat and Theta_CW decrease with increasing x and remain constant after x = 0.5. The magnetism appears poised between localized and itinerant behavior, and becomes more localized with increasing x as evidenced by the evolution of the Rhodes-Wohlfarth ratio. mu_eff per Ru is equal to the quenched spin-only S value across the entire solid solution. Comparisons with Sr(1-x)Ca(x)RuO3 reinforce the important role of structural distortions in determining magnetic ground state. It is suggested that electrical transport and magnetic properties are not strongly coupled in this system

Site-Net: Using global self-attention and real-space supercells to capture long-range interactions in crystal structures

Site-Net is a transformer architecture that models the periodic crystal structures of inorganic materials as a labelled point set of atoms and relies entirely on global self-attention and geometric information to guide learning. Site-Net processes standard crystallographic information files to generate a large real-space supercell, and the importance of interactions between all atomic sites is flexibly learned by the model for the prediction task presented. The attention mechanism is probed to reveal Site-Net can learn long-range interactions in crystal structures, and that specific attention heads become specialized to deal with primarily short- or long-range interactions. We perform a preliminary hyperparameter search and train Site-Net using a single graphics processing unit (GPU), and show Site-Net achieves state-of-the-art performance on a standard band gap regression task.Comment: 23 pages, 13 figure

Tilt engineering of spontaneous polarization and magnetization above 300 K in a bulk layered perovskite

Crystalline materials that combine electrical polarization and magnetization could be advantageous in applications such as information storage, but these properties are usually considered to have incompatible chemical bonding and electronic requirements. Recent theoretical work on perovskite materials suggested a route for combining both properties. We used crystal chemistry to engineer specific atomic displacements in a layered perovskite, (CaySr1–y)1.15Tb1.85Fe2O7, that change its symmetry and simultaneously generate electrical polarization and magnetization above room temperature. The two resulting properties are magnetoelectrically coupled as they arise from the same displacements

Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials

Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation

Visible Light Photo-oxidation of Model Pollutants Using CaCu3Ti4O12: An Experimental and Theoretical Study of Optical Properties, Electronic Structure, and Selectivity

[Image: see text] Charge transfer between metal ions occupying distinct crystallographic sublattices in an ordered material is a strategy to confer visible light absorption on complex oxides to generate potentially catalytically active electron and hole charge carriers. CaCu(3)Ti(4)O(12) has distinct octahedral Ti(4+) and square planar Cu(2+) sites and is thus a candidate material for this approach. The sol−gel synthesis of high surface area CaCu(3)Ti(4)O(12) and investigation of its optical absorption and photocatalytic reactivity with model pollutants are reported. Two gaps of 2.21 and 1.39 eV are observed in the visible region. These absorptions are explained by LSDA+U electronic structure calculations, including electron correlation on the Cu sites, as arising from transitions from a Cu-hybridized O 2p-derived valence band to localized empty states on Cu (attributed to the isolation of CuO(4) units within the structure of CaCu(3)Ti(4)O(12)) and to a Ti-based conduction band. The resulting charge carriers produce selective visible light photodegradation of 4-chlorophenol (monitored by mass spectrometry) by Pt-loaded CaCu(3)Ti(4)O(12) which is attributed to the chemical nature of the photogenerated charge carriers and has a quantum yield comparable with commercial visible light photocatalysts

Sidechain control of porosity closure in multiple peptide-based porous materials by cooperative folding

Porous materials find application in separation, storage and catalysis. We report a crystalline porous solid formed by coordination of metal centres with a glycylserine dipeptide. We prove experimentally that the structure evolves from a solvated porous into a non-porous state as result of ordered displacive and conformational changes of the peptide that suppress the void space in response to environmental pressure. This cooperative closure, which recalls the folding of proteins, retains order in three-dimensions and is driven by the hydroxyl groups acting as H-bond donors in the peptide sequence through the serine residue. This ordered closure is also displayed by multipeptide solid solutions in which the combination of different sequences of amino acids controls their guest response in a non-linear way. This functional control can be compared to the effect of single point mutations in proteins, where the exchange of single amino acids can radically alter structure and functio

Sponge-Like Behaviour in Isoreticular Cu(Gly-His-X) Peptide-Based Porous Materials

We report two isoreticular 3D peptide-based porous frameworks formed by coordination of the tripeptides Gly-l-His-Gly and Gly-l-His-l-Lys to Cu(II) which display sponge-like behaviour. These porous materials undergo structural collapse upon evacuation that can be reversed by exposure to water vapour, which permits recovery of the original open channel structure. This is further confirmed by sorption studies that reveal that both solids exhibit selective sorption of H(2)O while CO(2) adsorption does not result in recovery of the original structures. We also show how the pendant aliphatic amine chains, present in the framework from the introduction of the lysine amino acid in the peptidic backbone, can be post-synthetically modified to produce urea-functionalised networks by following methodologies typically used for metal–organic frameworks built from more rigid “classical” linkers

Tailoring the surface charge of dextran-based polymer coated SPIONs for modulated stem cell uptake and MRI contrast

Tracking stem cells in vivo using non-invasive techniques is critical to evaluate the efficacy and safety of stem cell therapies. Superparamagnetic iron oxide nanoparticles (SPIONs) enable cells to be tracked using magnetic resonance imaging (MRI), but to obtain detectable signal cells need to be labelled with a sufficient amount of iron oxide. For the majority of SPIONs, this can only be obtained with the use of transfection agents, which can adversely affect cell health. Here, we have synthesised a library of dextran-based polymer coated SPIONs with varying surface charge from −1.5 mV to +18.2 mV via a co-precipitation approach and investigated their ability to be directly internalised by stem cells without the need for transfection agents. The SPIONs were colloidally stable in physiological solutions. The crystalline phase of the particles was confirmed with powder X-ray diffraction and their magnetic properties were characterised using SQUID magnetometry and magnetic resonance. Increased surface charge led to six-fold increase in uptake of particles into stem cells and higher MRI contrast, with negligible change in cell viability. Cell tracking velocimetry was shown to be a more accurate method for predicting MRI contrast of stem cells compared to measuring iron oxide uptake through conventional bulk iron quantification

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

At the high level, the fundamental differences between materials originate from the unique nature of the constituent chemical elements. Before specific differences emerge according to the precise ratios of elements (composition) in a given crystal structure (phase), the material can be represented by its phase field defined simply as the set of the constituent chemical elements. Classification of the materials at the level of their phase fields can accelerate materials discovery by selecting the elemental combinations that are likely to produce desirable functional properties in synthetically accessible materials. Here, we demonstrate that classification of the materials phase field with respect to the maximum expected value of a target functional property can be combined with the ranking of the materials synthetic accessibility. This end-to-end machine learning approach (PhaseSelect) first derives the atomic characteristics from the compositional environments in all computationally and experimentally explored materials and then employs these characteristics to classify the phase field by their merit. PhaseSelect can quantify the materials potential at the level of the periodic table, which we demonstrate with significant accuracy for three avenues of materials applications: high-temperature superconducting, high-temperature magnetic and targetted energy band gap materials