The discovery of high-performance catalysts for energy conversion and environmental remediation has long been hindered by the vast compositional space and complex structure-property relationships in oxide materials. Traditional trial-and-error approaches are time-consuming and resource-intensive, often failing to identify optimal compositions beyond conventional stoichiometries. This study presents a machine learning-driven, high-throughput framework that enables the rapid identification and synthesis of non-stoichiometric oxide catalysts with superior activity and stability. By integrating predictive models trained on over 10,000 experimental data points—including thermodynamic stability, electronic structure, surface reactivity, and catalytic performance—our approach identifies promising candidate materials from a theoretical space of more than 10⁶ possible compositions. The top-performing candidates were synthesized using combinatorial thin-film deposition and validated through automated screening under real reaction conditions. Among the discovered materials, a Ni₀.₈₃Co₀.₁₇Oₓ catalyst exhibited a 4.2-fold enhancement in oxygen evolution reaction (OER) activity compared to benchmark IrO₂, while maintaining exceptional durability over 1,000 hours of operation.
The machine learning pipeline begins with feature engineering based on elemental properties, crystallographic descriptors, and DFT-calculated parameters such as oxygen vacancy formation energy, d-band center position, and adsorption energies for key intermediates. A gradient boosting regressor was trained to predict OER onset potential, while a convolutional neural network was used to classify structural stability and phase purity. Cross-validation revealed high predictive accuracy (R² = 0.93 for onset potential), enabling reliable ranking of candidate compositions. The model prioritized non-stoichiometric systems with controlled cationic disorder and oxygen excess, which are typically overlooked in conventional design strategies. From a pool of 50,000 virtual compositions, 15 were selected for experimental validation, including ternary oxides based on Ni-Co-Mn and Ni-Fe-Cu systems.838818-26-1 medchemexpress
Combinatorial sputtering was employed to deposit libraries of thin films across a wide range of stoichiometries in a single run.959122-11-3 Biological Activity High-throughput characterization using micro-XRD, XPS, and scanning electron microscopy enabled rapid phase identification and composition mapping.PMID:29083747 Electrochemical testing was conducted using an automated flow cell system equipped with integrated potentiostat and mass spectrometer, allowing real-time monitoring of O₂ production during OER. The most active sample, Ni₀.₈₃Co₀.₁₇Oₓ, showed a low overpotential of 287 mV at 10 mA/cm², outperforming both NiO and Co₃O₄ by significant margins. In situ XPS confirmed the presence of mixed oxidation states (Ni²⁺/Ni³⁺ and Co²⁺/Co³⁺) and oxygen vacancies, which facilitate charge transfer and lower the kinetic barrier for water oxidation.
Further analysis revealed that the enhanced activity stems from synergistic effects between Ni and Co sites, where Ni promotes hole localization and Co stabilizes high-valent oxy-metal species. The non-stoichiometric nature of the material allows dynamic reconstruction of the surface under operating conditions, forming a highly active amorphous layer enriched in oxygen vacancies and hydroxyl groups. This adaptive surface is critical for maintaining catalytic efficiency during prolonged operation. Molecular dynamics simulations support this hypothesis, showing that oxygen vacancies migrate and reorganize in response to applied potential, creating transient active centers.
This work demonstrates the power of machine learning to transcend traditional stoichiometric constraints and unlock new classes of functional materials. By coupling predictive modeling with high-throughput synthesis and testing, we accelerate the discovery process from decades to weeks. The framework is broadly applicable to other catalytic reactions such as CO₂ reduction, hydrogen evolution, and ammonia synthesis. Future extensions will incorporate feedback loops for iterative refinement and integrate robotic platforms for fully autonomous experimentation. Ultimately, this approach paves the way for a new era of intelligent materials science, where AI-guided design leads to breakthroughs in sustainable energy technologies.MedChemExpress (MCE) offers a wide range of high-quality research chemicals and biochemicals (novel life-science reagents, reference compounds and natural compounds) for scientific use. We have professionally experienced and friendly staff to meet your needs. We are a competent and trustworthy partner for your research and scientific projects.Related websites: https://www.medchemexpress.com