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This study combines density functional theory (DFT) and ab initio thermodynamics calculations with X-ray photoelectron spectroscopy (XPS) investigations to identify the reduction properties of Fe2O3 (001) surface with implications for corrosion resistance, hydrogen transport, and energy safety. Ab initio thermodynamics modeling predicts fully hydroxylated surface stability across a broad range of pressures (1x10-23 to 1x105 mbar) and temperatures below 700K, consistent with previous experimental studies. Above 800K, exposures to 1x10-4 mbar H2, 1x10-4 mbar O2, or 1x10-4 mbar H2 + 1x10-4 mbar O2 each yield unique XPS signals indicating a loss of -OH coverage, aligning with DFT predictions. Insight into the mechanism of reduction as function of H2 exposure is provided, as well as conditions that promote further reduction towards Fe3O4. Theoretical and experimental investigations indicate the ability to maintain the Fe2O3 protective layer of iron oxides that have been exposed to H2 environments by including trace amounts of O2. 

Abstract The versatile Bell-Evans-Polanyi (BEP) relation stipulates the kinetics of a reaction in terms of thermodynamics. Herein, we establish the BEP relation for the hydrogen evolution reaction (HER) from fundamental electrochemical principles leveraging the Butler-Volmer relation for a one-step, one-electron process and the transition state theory. Based on first-principles investigations of HER mechanisms on fourteen metal electrodes, we firmly justify the BEP relation solely using an easy-to compute hydrogen adsorption free energy and universal electrochemical constants. 

Understanding how materials melt is crucial for their practical applications and development, machine learning atomistic potentionals are enabling us to better predict these behaviors in real-world environmental conditions. 

Machine learning atomistic potentials (MLPs) trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost.