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Not AvailableThe surface chemistry of metal oxides and their catalytic roles in activating and transforming oxygenate and hydrocarbon feedstocks is rich. This review provides an update on mechanisms of such reactions, as well as modern promising concepts for selective catalytic conversions on metal oxide catalysts, including those occurring in molecularly confining reaction environments. Case examples built upon electronic structure modeling of transition states are emphasized, as well as on contemporary ideas to enable low free energies of activation. The chemistry covered is broad, and includes examples of Lewis acid–base chemistry, Brønsted acid– base chemistry, and oxygen vacancy-based redox chemistry. The reactions covered include C-H activation of alkanes, C–C coupling of aldehydes, C–C coupling of carboxylic acids (ketonization), dehydration of alcohols over confined sites, C–C bond formation between aldehydes over confined sites, and C–C bond formation between carboxylic acids over confined sites. For each reaction type, molecular-level knowledge of elementary reaction steps is critically reviewed. 

Overview of the MatterTune framework, which enables flexible and accurate fine-tuning of pre-trained atomistic foundation models for materials science. 

The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces. While their capability for modeling phonon properties is emerging, systematic benchmarking across chemically diverse systems remains limited. We evaluate six recent uMLPs—EquiformerV2, MatterSim, MACE, and CHGNet—on 2429 crystalline materials from the Open Quantum Materials Database. Models were used to compute atomic forces in displaced supercells, derive interatomic force constants (IFCs), and predict phonon properties including lattice thermal conductivity (LTC), compared with density functional theory and experimental data. The EquiformerV2 pretrained model trained on the OMat24 dataset exhibits strong performance in predicting atomic forces and third‐order IFCs, while its fine‐tuned counterpart consistently outperforms other models in predicting second‐order IFCs, LTC, and other phonon properties. Although MACE and CHGNet demonstrated comparable force prediction accuracy to EquiformerV2, notable discrepancies in IFC fitting led to poor LTC predictions. Conversely, MatterSim, despite lower force accuracy, achieved intermediate IFC predictions, suggesting error cancellation and complex relationships between force accuracy and phonon predictions. This benchmark guides the evaluation and selection of uMLPs for high‐throughput screening of materials with targeted thermal transport properties.