Abstract During the exploration of the interface between the known structures of perovskite Y2NiTiO6 and hexagonal layered Y2CuTiO6, we have discovered the new phase Y10NixCu1–xTi4O24 (x = 0, 0.5, 1). The structure of Y10CuTi4O24 was solved by means of single-crystal X-ray diffraction, which revealed a layered monoclinic structure, with the space group C2/m, a = 12.2405(1), b = 5.8643(1), c = 7.1729(1) Å, β = 107.083(1)°. The structures of three Y10NixCu1–xTi4O24 (x = 0, 0.5, 1) phases were also refined based on high-resolution powder X-ray diffraction data. Substitution of Cu for Ni causes only minor changes in lattice and atomic parameters. The new phase is related to known Y5Mo2O12-type structures with an extra atomic position occupied by Ni/Cu in the structure of Y10NixCu1–xTi4O24 (x = 0, 0.5, 1). The high-resolution powder X-ray diffraction data revealed peak broadening for the reflections with l = 2n + 1 corresponding to stacking faults originating from the layered structure of Y10NixCu1–xTi4O24. Y10NixCu1–xTi4O24 (x = 0, 0.5, 1) were characterized with respect to their magnetic and optical properties.

During the exploration of the interface between the known structures of perovskite Y2NiTiO6 and hexagonal layered Y2CuTiO6, we have discovered the new phase Y10NixCu1-xTi4O24 (x = 0, 0.5, 1). The structure of Y10CuTi4O24 was solved by means of single-crystal X-ray diffraction, which revealed a layered monoclinic structure, with the space group C2/m, a = 12.2405(1), b = 5.8643(1), c = 7.1729(1) Å, β = 107.083(1)°. The structures of three Y10NixCu1-xTi4O24 (x = 0, 0.5, 1) phases were also refined based on high-resolution powder X-ray diffraction data. Substitution of Cu for Ni causes only minor changes in lattice and atomic parameters. The new phase is related to known Y5Mo2O12-type structures with an extra atomic position occupied by Ni/Cu in the structure of Y10NixCu1-xTi4O24 (x = 0, 0.5, 1). The high-resolution powder X-ray diffraction data revealed peak broadening for the reflections with l = 2n + 1 corresponding to stacking faults originating from the layered structure of Y10NixCu1-xTi4O24. Y10NixCu1-xTi4O24 (x = 0, 0.5, 1) were characterized with respect to their magnetic and optical properties.

Abstract All-solid-state batteries require solid electrolytes with sufficient oxidative and reductive stability; however, the interfacial behavior of emerging silicon-based sulfide electrolytes remains poorly understood. In particular, the electrochemical stability of Li7Si2S7I under practical cell operation and the separate effects of cathode and anode interfaces on performance degradation have not been clarified. Here, we systematically evaluate Li7Si2S7I in LiNi0.82Mn0.07Co0.11O2 (NMC82) | solid electrolyte | Li-In all-solid-state cells using two- and three-electrode configurations combined with impedance analysis and ex situ Raman and X-ray photoelectron spectroscopy. Three-electrode measurements decouple the interfacial reactions and reveal concurrent interfacial decomposition at both the cathode and anode sides, with dominating effect from the cathode side. Symmetric cell tests using Li-In/Li7Si2S7I composite electrodes confirm slight reductive instability against Li-In for Li7Si2S7I as an anolyte. Spectroscopic analysis identifies polysulfides, oxidized iodide, and oxygenated SiOx and SOx species at the Li7Si2S7I/NMC82 interface, as well as In2S3 formation at the Li7Si2S7I/Li-In interface. Our findings provide mechanistic insight into the interfacial reactions of Si-based sulfide electrolytes and highlight the importance of electrode-decoupled diagnostics for evaluating the compatibility with electrode materials in all-solid-state batteries.

[This corrects the article DOI: 10.1021/acs.chemmater.4c00188.].

ABSTRACT Transparent Conducting Oxides (TCOs) are essential as electrodes in a broad range of optoelectronic applications. A range of n‐type TCOs meeting application requirements for transparency and conductivity have been developed, whereas efficient and stable p‐type TCOs have so far remained a challenge, limiting the opportunities to explore novel architectures for optoelectronic devices. In particular, a high‐performance p‐type transparent electrode would enable the use of n‐type absorber layers in superstrate solar cells. Recently, correlated metals featuring strong interelectron repulsion have enabled enhanced transparency in highly conducting n‐type systems leading to figures of merit comparable with those of conventional degenerate semiconductors such as ITO. Here we demonstrate that the p‐type correlated metal LaNiO 3 is a robust and scalable p‐type transparent conductor with excellent performance compared to existing materials. We achieve a Haacke Figure of Merit more than double, and Anand Exact Figure of Merit 1.4 times that of the respective highest performing p‐type TCOs, at thicknesses ≤12 nm. For a free‐standing 3 nm film, we obtained a minimal sheet resistance of 1070 Ω/□ and transmittance of >85%. The films are air stable and show a thickness‐tuneable balance of sheet resistance and transparency which is critical for targeted applications.

During the exploration of the interface between the known structures of perovskite Y2NiTiO6 and hexagonal layered Y2CuTiO6, we have discovered the new phase Y10Ni x Cu1-x Ti4O24 (x = 0, 0.5, 1). The structure of Y10CuTi4O24 was solved by means of single-crystal X-ray diffraction, which revealed a layered monoclinic structure, with the space group C2/m, a = 12.2405(1), b = 5.8643(1), c = 7.1729(1) & Aring;, beta = 107.083(1)degrees. The structures of three Y10Ni x Cu1-x Ti4O24 (x = 0, 0.5, 1) phases were also refined based on high-resolution powder X-ray diffraction data. Substitution of Cu for Ni causes only minor changes in lattice and atomic parameters. The new phase is related to known Y5Mo2O12-type structures with an extra atomic position occupied by Ni/Cu in the structure of Y10Ni x Cu1-x Ti4O24 (x = 0, 0.5, 1). The high-resolution powder X-ray diffraction data revealed peak broadening for the reflections with l = 2n + 1 corresponding to stacking faults originating from the layered structure of Y10Ni x Cu1-x Ti4O24. Y10Ni x Cu1-x Ti4O24 (x = 0, 0.5, 1) were characterized with respect to their magnetic and optical properties.

The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.

Correction for 'Enhanced performance in transparent conducting materials at the interface of a wide band gap semiconductor and a correlated metal' by Jessica L. Stoner et al., Mater. Horiz., 2025, 12, 5820-5828, https://doi.org/10.1039/d5mh00283d.

We report the experimental discovery of Li7Si2S7I0.89Cl0.11, a triclinic (P1̄) analogue of the recently discovered monoclinic (P21/n) Li7Si2S7I (LSSI), which retains the high computationally predicted Li+ ion conductivity of LSSI. Li7Si2S7I0.89Cl0.11, which is effectively a polymorph of LSSI, maintains the same ordered anion packing based on the packing of spheres in the NiZr intermetallic, however demonstrates a distinct ordering of the Si4+ framework-forming cations. While both structures feature Si2S7 dimers within hexagonal close-packed (hcp) anion motifs, their different arrangement in the triclinic material results in the alternating stacking of silicon-free and silicon-rich layers. Li7Si2S7I0.89Cl0.11 has an additional Li+ position compared to LSSI, sixteen in total, which maintains the large number of redundant low energy pathways favourable for superionic conduction. Thus, Li7Si2S7I0.89Cl0.11 has a predicted ionic conductivity of 0.019(7) S cm-1 derived from molecular dynamics simulation of the experimentally measured structure and a theoretical activation energy for bulk Li+ ion transport of 0.16(4) eV, within error of those of monoclinic LSSI. These results demonstrate the structural resilience of the ordered S2-/I- anion net to changes in cation positions and crystal system, further exemplifying the ability of the net to afford diverse low barrier Li+ ion transport pathways and thus generate a predicted superionic conductivity. Polymorphism is generally thought to have a profound impact on ion transport, however the computational results here suggest that there are privileged anion frameworks with an intrinsic robustness to changes in cation distribution where superionic transport can persist.

We explore multiple-cation chalco-halide phase fields evaluated by their synthetic accessibility using machine learning models. Exploratory synthesis guided by computational tools leads to the discovery of two new compounds; CuSn2SI3 and Cu0.35Sn5.29S2I7, their structures, and electronic and optical properties are reported herein. This is the first report of a stable quaternary compound in the Cu-Sn-S-I phase field. The two new compounds show related crystal structures where Sn4S2I4 layers are a common structural motif in both. These Sn4S2I4 layers are connected by Cu2I2 layers and disordered Cu-Sn-I layers, forming the three-dimensional structures of CuSn2SI3 and Cu0.35Sn5.29S2I7 respectively. Electronic band structure calculations using density functional theory show the presence of a direct band gap in CuSn2SI3 and suggest anisotropic transport, in line with the layered structure of the compound. A mixture of the two compounds with similar to 86% CuSn2SI3, shows a band gap in the visible region, close to 2.1 eV and a significant photo-induced charge carrier mobility of similar to 1.3 cm2 V-1 s-1. This demonstrates Cu-Sn chalco-halides can form a promising phase space to explore for solar absorber materials, with further design and tuning of band gap.

Sulfide lithium argyrodites are a key materials family that are studied as solid electrolytes in commercial all-solid-state batteries (ASSBs), while their oxide analogues remain relatively unexplored. This study presents the discovery of Li7TiO5X (X = Cl-, Br-), the first lithium argyrodite materials in which a transition metal is used as the framework-forming cation, expanding the chemical space that is accessible for oxide argyrodites. Incorporation of Ti4+ enables the lithium content to be maximized to 7 Li+ per formula unit. Interestingly, even with the high lithium content, Li7TiO5Cl retains a Li+ site disordered cubic F4̅3m structure at room temperature with Li+ occupancy of the T5, T5a, and T3 positions, and exhibits an ionic conductivity of 2.2(2) × 10-6 S cm-1 with the lowest reported activation energy (0.36(2) eV) for bulk Li+ ion transport in an oxide argyrodite. Conversely, Li7TiO5Br adopts the same F4̅3m symmetry at room temperature but with an ordered arrangement of Li+ positions via full occupancy of the T5a and T3 positions, and thus has an ionic conductivity that is 3 orders of magnitude lower (∼10-9 S cm-1) and a much higher activation energy (0.58(2) eV) than Li7TiO5Cl. Order-disorder behavior is observed below 250 K in Li7TiO5Cl, where a Li+ site ordering pattern is observed that is distinct from Li7TiO5Br and all sulfide argyrodites, yielding a tetragonal symmetry (I4̅) for only the second time to date in the argyrodite structure type. This unique order-disorder behavior, alongside the ability to incorporate transition metal cations within this material family emphasizes the potential to access much greater structural diversity via the expansive chemical space that is available for exploration in oxide argyrodites.

The exploration of higher-dimensional chemical phase spaces and the synthesis of novel compounds can be achieved by applying a multiple-anion approach to materials discovery. The ability to combine and tune the stoichiometry of anions in a material can enable enhanced control of both the physical and electronic structures, providing a strategy for the modification of the properties of new materials being developed for a variety of applications, including solar absorbers and thermoelectrics. Here, we report the synthesis of Cu7.62Bi6Se12Cl6I, a quadruple-anion (Se2-, (Se2)2-, Cl-, I-) material within the Cu-Bi-Se-Cl-I phase space. Crystal growth reactions yield black, needle-like crystals, which exhibit a highly anisotropic and complex structure containing the four distinct anion types, solved from single-crystal X-ray diffraction data. Compositional analysis confirms the complex material stoichiometry, and a low band gap of 0.94(5) eV is measured to understand the potential for solar-absorbing applications. Cu7.62Bi6Se12Cl6I has a low thermal conductivity of 0.25(2) W K-1 m-1, which is attributed to multiple structural features via analysis of experimental heat capacity data and is achieved through the diversity in bonding that is accessed through the combination of four different types of anion.

The catalytic hydrogenolysis process offers the selective production of high-value liquid alkanes from waste polymers. Herein, through normalisation of Ni structure, Ni mass and density, and CeO2 crystallite size, the importance of CeO2 nanocube morphology in the hydrogenolysis of polypropylene (M-w = 12 000 g mol(-1); M-n = 5000 g mol(-1)) over Ni/CeO2 catalysts was determined. High liquid productivities (65.9-70.9 g(liquid) g(Ni)(-1) h(-1)) and low methane yields (10%) were achieved over two different Ni/CeO2 catalysts after 16 h reaction due to the high activity and internal scission selectivity of the supported ultrafine Ni particles (<1.3 nm). However, the Ni/CeO2 nanocube catalyst exhibited higher C-C scission rates (838.1 mmol g(Ni)(-1) h(-1)) than a standard benchmark mixed shape Ni/CeO2 catalyst (480.3 mmol g(Ni)(-1) h(-1)) and represents a 75% increase in depolymerisation activity. This led to shorter hydrocarbon chains achieved by the nanocube catalyst (M-w = 2786 g mol(-1); M-n = 1442 g mol(-1)) when compared to the mixed shape catalyst (M-w = 4599 g mol(-1); M-n = 2530 g mol(-1)). The enhanced C-C scission rate of the nanocube catalyst was determined to arise from a combination of improved H-storage and favourable basic properties, with higher weak basic site density key to facilitate a greater degree of hydrocarbon chain adsorption.

Computational modelling of materials using machine learning (ML) and historical data has become integral to materials research across physical sciences. The accuracy of predictions for material properties using computational modelling is strongly affected by the choice of the numerical representation that describes a material's composition, crystal structure and constituent chemical elements. Structure, both extended and local, has a controlling effect on properties, but often only the composition of a candidate material is available. However, existing elemental and compositional descriptors lack direct access to structural insights such as the coordination geometry of an element. In this study, we introduce Local Environment-induced Atomic Features (LEAFs), which incorporate information about the statistically preferred local coordination geometry at an element in a crystal structure into descriptors for chemical elements, enabling the modelling of materials solely as compositions without requiring knowledge of their crystal structure. In the crystal structure of a material, each atomic site can be quantitatively described by similarity to common local structural motifs; by aggregating these unique features of similarity from the experimentally verified crystal structures of inorganic materials, LEAFs formulate a set of descriptors for chemical elements and compositions. The direct connection of LEAFs to the local coordination geometry enables the analysis of ML model property predictions, linking compositions to the underlying structure-property relationships. We demonstrate the versatility of LEAFs in structure-informed property predictions for compositions, mapping of chemical space in structural terms, and prioritisation of elemental substitutions. Based on the latter for predicting crystal structures of binary ionic compounds, LEAFs achieve the state-of-the-art accuracy of 86%. These results suggest that the structurally informed description of chemical elements and compositions developed in this work can effectively guide synthetic efforts in discovering new materials.

The scarcity of property labels remains a key challenge in materials informatics, whereas materials data without property labels are abundant in comparison. By pre-training supervised property prediction models on self-supervised tasks that depend only on the “intrinsic information” available in any Crystallographic Information File (CIF), there is potential to leverage the large amount of crystal data without property labels to improve property prediction results on small datasets. We apply Deep InfoMax as a self-supervised machine learning framework for materials informatics that explicitly maximises the mutual information between a point set (or graph) representation of a crystal and a vector representation suitable for downstream learning. This allows the pre-training of supervised models on large materials datasets without the need for property labels and without requiring the model to reconstruct the crystal from a representation vector. We investigate the benefits of Deep InfoMax pre-training implemented on the Site-Net architecture to improve the performance of downstream property prediction models with small amounts (<103) of data, a situation relevant to experimentally measured materials property databases. Using a property label masking methodology, where we perform self-supervised learning on larger supervised datasets and then train supervised models on a small subset of the labels, we isolate Deep InfoMax pre-training from the effects of distributional shift. We demonstrate performance improvements in the contexts of representation learning and transfer learning on the tasks of band gap and formation energy prediction. Having established the effectiveness of Deep InfoMax pre-training in a controlled environment, our findings provide a foundation for extending the approach to address practical challenges in materials informatics.

Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face constraints imposed by the quantity and quality of available data. Moreover, ML is often employed in tandem with simulated datasets originating from density functional theory (DFT), and assessed through in-sample evaluation schemes. This scenario raises questions about the practical utility of ML in uncovering new and significant material classes for industrial applications. Here, we propose a data-driven framework aimed at accelerating the discovery of new transparent conducting materials (TCMs), an important category of semiconductors with a wide range of applications. To mitigate the shortage of available data, we create and validate unique experimental databases, comprising several examples of existing TCMs. We assess state-of-the-art (SOTA) ML models for property prediction from the stoichiometry alone. We propose a bespoke evaluation scheme to provide empirical evidence on the ability of ML to uncover new, previously unseen materials of interest. We test our approach on a list of 55 compositions containing typical elements of known TCMs. Although our study indicates that ML tends to identify new TCMs compositionally similar to those in the training data, we empirically demonstrate that it can highlight material candidates that may have been previously overlooked, offering a systematic approach to identify materials that are likely to display TCMs characteristics.

The first reported phase in the Y2O3-NiO-TiO2 chemical space, the Y2NiTiO6 perovskite undergoes a temperature-induced order-disorder transition. Above ∼1700 K, it adopts the structure of a disordered CaTiO3-type orthorhombic perovskite with a = 5.26939(2), b = 5.60367(2), and c = 7.58137(3) Å, with the B site uniformly occupied by 0.5Ni+0.5Ti. Below this temperature, Y2NiTiO6 adopts rock-salt ordering of the transition metals in a monoclinic unit cell (a = 5.26695(2), b = 5.60164(2), c = 7.57493(2) Å, β = 90.4940(2)°) with 0.9/0.1 ordering of the B site. Ordering of Ni and Ti changes the magnetic properties from spin-glass behavior in the orthorhombic phase to antiferromagnetic order (TN = 17 K) for the monoclinic phase, while the optical properties of both phases remain unchanged across the transition.

Several classes of inorganic transparent conducting coatings are available (broad band wide band gap semiconductors, noble metals, amorphous oxides and correlated metals), with peak performance depending on the layer thickness. Correlated metallic transition metal oxides have emerged as potential competitive materials for small coating thicknesses, but their peak performance remains one order of magnitude below other best in class materials. By exploiting the charge transfer at the interface between a correlated metal (SrNbO3) and a wide band gap semiconductor (SrTiO3), we show that pulsed laser deposition-grown SrNbO3 heterostructures on SrTiO3 outperform correlated metals by an order of magnitude. The apparent increase in carrier concentration confirms that an electronically active interfacial layer is contributing to the transport properties of the heterostructure. The correlated metallic electrode allows the extraction of high mobility carriers resulting in enhanced conductivity for heterostructures with thicknesses up to 20 nm. The high optical absorption of the high mobility metallic interface does not have a detrimental effect on the transmission of the heterostructure due to its small thickness. The charge transfer-driven enhanced electrical properties in correlated metal - wide band gap semiconductor heterostructures offer a distinct route to high performance transparent conducting materials.

The discovery of new materials often requires collaboration between experimental and computational chemists. Web based platforms allow more flexibility in this collaboration by giving access to computational tools without the need for access to computational researchers. We present Liverpool Materials Discovery Server (lmds.liverpool.ac.uk), one such platform which currently hosts six state of the art computational tools in an easy to use format. We describe the development of this platform, highlighting the advantages and disadvantages the methods used. In addition, we provide source code, and setup scripts to enable other research groups to create similar platforms, to promote collaboration both within and between research groups.

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user-specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen's outputs are fully interpretable and provably correct. Users interact with Comgen through a high-level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end-to-end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation. This highlights the potential of automated formal reasoning in materials chemistry.