Troy D. Manning

Functional oxides are widely used in electronic applications, where the temperature dependance of electrical conductivity is a key metric for material suitability. Experimental characterization of these properties remains resource‐intensive and time‐consuming, so machine learning has emerged as an alternative to accelerate materials discovery processes. However, data‐driven approaches are restricted by the lack of a comprehensive experimentally sourced database, with existing models trained on simulated density‐functional theory (DFT) calculations. Here, an experimental database of approximately 8000 temperature‐dependent conductivity samples for oxides is compiled from commercial databases and peer‐reviewed literature. MODNet, a state‐of‐the‐art general purpose composition‐based baseline, is benchmarked against TriCondNet, a physics‐guided neural network (PGNN) developed to capture temperature‐dependent conductivity behavior of metallic and semiconducting oxides. TriCondNet outperforms baseline models on standard error metrics while adhering to the physical models for conductivity–temperature dependance. The top‐performing model is then applied to an extensive composition list, screening candidates for two application‐specific case studies: dielectric and thermoelectric materials. Ranked by their predicted conductivity and associated uncertainty, the top candidates match materials already recognized as leading dielectrics and thermoelectrics. These results indicate that the workflow can recover established high‐performers, supporting the use of TriCondNet as a basis for identifying new oxide candidates through composition interpolation.

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.

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.

New compounds are discovered in the under-explored d ¹⁰ –s ² (Cu–Sn) family using exploratory synthesis guided by computational tools. Band-gaps in the visible region with moderate charge-carrier mobilities make these potential solar absorbers.

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 (T_N = 17 K) for the monoclinic phase, while the optical properties of both phases remain unchanged across the transition.

We curate experimental datasets and use them to train ML models for accelerating the identification of transparent conductors, demonstrating how ML can reveal candidates that may have been previously overlooked as viable transparent conductors.

PLD prepared SrNbO ₃ thin films on SrTiO ₃ show enhanced carrier concentrations and mobilities due to charge transfer at the interface. Their performance surpasses optimised correlated metals and rivals doped semiconductors, even at lower thicknesses.

The nanocube support morphology improves C–C scission rates of Ni/CeO₂ catalysts to generate shorter chain liquid hydrocarbon mixtures in the catalytic hydrogenolysis process.

K₃coronene is computationally predicted and synthesised. Crystal structure solution reveals a highly disordered structure with evidence of local spin-singlet formation from magnetic property and EPR measurements.