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The correlated semiconductor vanadium dioxide (VO2) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows. [ABSTRACT FROM AUTHOR]

IrO2 is one of the most efficient electrocatalysts for the oxygen evolution reaction (OER), and also has other applications such as in pH sensors. Atomistic modeling of IrO2 is critical for understanding the structure, chemistry, and nanoscale dynamics of IrO2 in these applications. Such modeling has remained elusive due to the lack of an empirical force field (EFF) for IrO2. We introduce a first-principles-based EFF that couples the Morse (MS) potential with a variable charge equilibration method, QEq. The EFF parameters are optimized using a genetic algorithm (GA) on a density functional theory (DFT)-based training set. The resultant Morse plus QEq EFF, “MS-Q” in short, successfully reproduces the lattice parameters, elastic constants, binding energies, and internal coordinates of various polymorphs of IrO2 from DFT calculations. More importantly, MS-Q accurately captures key metrics for evaluating structural and chemical properties of catalysts such as surface energetics, equilibrium shape, electrostatic charges, oxygen vacancy formation energies, relative stability of low index rutile IrO2 surfaces, and pressure-induced phase transformations. The MS-Q EFF is used to predict the oxygen binding energy (Ead), a well-known descriptor for OER activity, on various sites of a nanocatalyst. We find Ead to be more favorable at low coordination sites, i.e. edges and corners, compared to planar facets; Ead is also correlated with charge transfer between the adsorbed O and nanocrystal, highlighting the importance of variable charge electrostatics in modeling catalysis on metal oxide surfaces. Our variable charge force field offers encouraging prospects for carrying out large-scale reactive simulations to evaluate catalytic performance of IrO2 surfaces and nanostructures. [ABSTRACT FROM AUTHOR]