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Diamond electrochemistry is primarily influenced by quantities of sp3‐carbon, surface terminations, and crystalline structure. In this work, a new dimension is introduced by investigating the effect of using substrate‐interlayers for diamond growth. Boron and nitrogen co‐doped nanocrystalline diamond (BNDD) films are grown on Si substrate without and with Ti and Ta as interlayers, named BNDD/Si, BNDD/Ti/Si, and BNDD/Ta/Ti/Si, respectively. After detailed characterization using microscopies, spectroscopies, electrochemical techniques, and density functional theory simulations, the relationship of composition, interfacial structure, charge transport, and electrochemical properties of the interface between diamond and metal is investigated. The BNDD/Ta/Ti/Si electrodes exhibit faster electron transfer processes than the other two diamond electrodes. The interlayer thus determines the intrinsic activity and reaction kinetics. The reduction in their barrier widths can be attributed to the formation of TaC, which facilitates carrier tunneling, and simultaneously increases the concentration of electrically active defects. As a case study, the BNDD/Ta/Ti/Si electrode is further employed to assemble a redox‐electrolyte‐based supercapacitor device with enhanced performance. In summary, the study not only sheds light on the intricate relationship between interlayer composition, charge transfer, and electrochemical performance but also demonstrates the potential of tailored interlayer design to unlock new capabilities in diamond‐based electrochemical devices.
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.
