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Tuning the electronic properties of oxide surfaces is of pivotal importance, because they find applicability in a variety of industrial processes, including catalysis. Currently, the industrial protocols for synthesizing oxide surfaces are limited to only partial control of the oxide's properties. This is because the ceramic processes result in complex morphologies and a priori unpredictable behavior of the products. While the bulk doping of alumina surfaces has been demonstrated to enhance their catalytic applications ( i.e. hydrodesulphurization (HDS)), the fundamental understanding of this phenomenon and its effect at an atomic level remain unexplored. In our joint experimental and computational study, simulations based on Density Functional Theory (DFT), synthesis, and a variety of surface characterization techniques are exploited for the specific goal of understanding the structure–function relationship of phosphorus-doped γ-Al 2 O 3 surfaces. Our theoretical calculations and experimental results agree in finding that P doping of γ-Al 2 O 3 leads to a significant decrease in its work function. Our computational models show that this decrease is due to the formation of a new surface dipole, providing a clear picture of the effect of P doping at the surface of γ-Al 2 O 3 . In this study, we uncover a general paradigm for tuning support–catalyst interactions that involves electrostatic properties of doped γ-Al 2 O 3 surface, specifically the surface dipole. Our findings open a new pathway for engineering the electronic properties of metal oxides’ surfaces. 

In silico materials design is hampered by the computational complexity of Kohn–Sham DFT, which scales cubically with the system size. Owing to the development of new‐generation kinetic energy density functionals (KEDFs), orbital‐free DFT (OFDFT) can now be successfully applied to a large class of semiconductors and such finite systems as quantum dots and metal clusters. In this work, we present DFTpy, an open‐source software implementing OFDFT written entirely in Python 3 and outsourcing the computationally expensive operations to third‐party modules, such as NumPy and SciPy. When fast simulations are in order, DFTpy exploits the fast Fourier transforms from PyFFTW. New‐generation, nonlocal and density‐dependent‐kernel KEDFs are made computationally efficient by employing linear splines and other methods for fast kernel builds. We showcase DFTpy by solving for the electronic structure of a million‐atom system of aluminum metal which was computed on a single CPU. The Python 3 implementation is object‐oriented, opening the door to easy implementation of new features. As an example, we present a time‐dependent OFDFT implementation (hydrodynamic DFT) which we use to compute the spectra of small metal clusters recovering qualitatively the time‐dependent Kohn–Sham DFT result. The Python codebase allows for easy implementation of application programming interfaces. We showcase the combination of DFTpy and ASE for molecular dynamics simulations of liquid metals. DFTpy is released under the MIT license.

This article is categorized under:

Software > Quantum Chemistry

Electronic Structure Theory > Density Functional Theory

Data Science > Computer Algorithms and Programming

 

Quantum chemistry must evolve if it wants to fully leverage the benefits of the internet age, where the worldwide web offers a vast tapestry of tools that enable users to communicate and interact with complex data at the speed and convenience of a button press. The Open Chemistry project has developed an open‐source framework that offers an end‐to‐end solution for producing, sharing, and visualizing quantum chemical data interactively on the web using an array of modern tools and approaches. These tools build on some of the best open‐source community projects such as Jupyter for interactive online notebooks, coupled with 3D accelerated visualization, state‐of‐the‐art computational chemistry codes including NWChem and Psi4, and emerging machine learning and data mining tools such as ChemML and ANI. They offer flexible formats to import and export data, along with approaches to compare computational and experimental data.