The application of QM/MM simulations in heterogeneous catalysis

The QM/MM simulation method is provenly efficient for the simulation of biological systems, where an interplay of extensive environment and delicate local interactions drives a process of interest through a funnel on a complex energy landscape. Recent advances in quantum chemistry and force-field methods present opportunities for the adoption of QM/MM to simulate heterogeneous catalytic processes, and their related systems, where similar intricacies exist on the energy landscape. Herein, the fundamental theoretical considerations for performing QM/MM simulations, and the practical considerations for setting up QM/MM simulations of catalytic systems, are introduced; then, areas of heterogeneous catalysis are explored where QM/MM methods have been most fruitfully applied. The discussion includes simulations performed for adsorption processes in solvent at metallic interfaces, reaction mechanisms within zeolitic systems, nanoparticles, and defect chemistry within ionic solids. We conclude with a perspective on the current state of the field and areas where future opportunities for development and application exist

Surface hydrogenation of oxygen terminated MXenes M2CO2 (M = Ti, V, Nb)

We have investigated reduction of MXenes M2CO2 (M = Ti, V, Nb) surface by hydrogen using density functional theory and statistical physics methods. We have approximated lateral interactions between adsorbed hydrogen with simple pairwise potential. We have confirmed model stability via cross-validation. Adsorption isotherms are calculated using Metropolis Monte Carlo method. We have built analytical Langmuir-like approximation of calculated isotherms. Ordered phases with 1/3 and 2/3 ML coverage are visually observed at low temperatures. At temperatures above 300 K no obvious plateau is observed, and intermediate phases does not exist. We compared adsorptive properties of MXenes at the same external conditions

Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows toward object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create deep modular interfaces that connect big-data workflows and electronic structure codes and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; in another, DFTB+ receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+ or enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities

Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows towards object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create "deep" modular interfaces that connect big-data workflows and electronic structure codes, and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; and in another, DFTB+ receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+, or to enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities

Integrated workflows and interfaces for data-driven semi-empirical electronic structure calculations

Modern software engineering of electronic structure codes has seen a paradigm shift from monolithic workflows toward object-based modularity. Software objectivity allows for greater flexibility in the application of electronic structure calculations, with particular benefits when integrated with approaches for data-driven analysis. Here, we discuss different approaches to create deep modular interfaces that connect big-data workflows and electronic structure codes and explore the diversity of use cases that they can enable. We present two such interface approaches for the semi-empirical electronic structure package, DFTB+. In one case, DFTB+ is applied as a library and provides data to an external workflow; in another, DFTB+ receives data via external bindings and processes the information subsequently within an internal workflow. We provide a general framework to enable data exchange workflows for embedding new machine-learning-based Hamiltonians within DFTB+ or enabling deep integration of DFTB+ in multiscale embedding workflows. These modular interfaces demonstrate opportunities in emergent software and workflows to accelerate scientific discovery by harnessing existing software capabilities

Atomic Simulation Interface (ASI) : application programming interface for electronic structure codes

The Atomic Simulation Interface (ASI) is a native C-style API for density functional theory (DFT) codes. ASI provides an efficient way to import and export large arrays that describe electronic structure (e.g. Hamiltonian, overlap, and density matrices) from DFT codes that are typically monolithic. The ASI API is designed to be implemented and used with minimal performance penalty, avoiding, where possible, unnecessary data copying. It provides direct access to the internal data structures of a code, and reuses existing data distribution over MPI nodes. The ASI API also defines a set of functions that support classical, AIMD (ab initio molecular dynamics), and hybrid QM/MM simulations: exporting potential energy, forces, atomic charges, and electrostatic potential at user defined points, as well as importing nuclear coordinates and arbitrary external electrostatic potentials. The ASI API is implemented in the DFTB+ (Hourahine et al., 2020) and FHI-aims (Blum et al., 2009) codes. A Python wrapper for easy access to ASI functions is also freely available (asi4py). We hope that the ASI API will be widely adopted and used for development of universal and interoperable DFT codes without sacrificing efficiency for portability

Tuning the size of TiO2-supported Co nanoparticle Fischer-Tropsch catalysts using Mn additions

Modifying traditional Co/TiO2-based Fischer–Tropsch (FT) catalysts with Mn promoters induces a selectivity shift from long-chain paraffins toward commercially desirable alcohols and olefins. In this work, we use in situ gas cell scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) elemental mapping, and near-ambient pressure X-ray photoelectron spectroscopy (NAP-XPS) to demonstrate how the elemental dispersion and chemical structure of the as-calcined materials evolve during the H2 activation heat treatment required for industrial CoMn/TiO2 FT catalysts. We find that Mn additions reduce both the mean Co particle diameter and the size distribution but that the Mn remains dispersed on the support after the activation step. Density functional theory calculations show that the slower surface diffusion of Mn is likely due to the lower number of energetically accessible sites for the Mn on the titania support and that favorable Co–Mn interactions likely cause greater dispersion and slower sintering of Co in the Mn-promoted catalyst. These mechanistic insights into how the introduction of Mn tunes the Co nanoparticle size can be applied to inform the design of future-supported nanoparticle catalysts for FT and other heterogeneous catalytic processes

Potential of lateral interactions of CO on Pt (111) fitted to recent STM images

Monolayers of carbon monoxide (CO) on Pt(111) surfaces are one of the most studied adsorption systems. However, molecular models of this system still do not take into account the reliable potential of lateral interactions between adsorbed CO molecules. Recent advances in experimental technique have brought high-resolution real-space images of CO/Pt(111) monolayers. For example, Yang et al. (J. Phys. Chem. C 117 (2013) 16429–16437) found island structures for coverages from 0.11 to 0.25 ML. In this study we have shown that these island structures can be explained with long-range oscillating lateral interactions. Parameters of the proposed potential were fitted to experimental scanning tunneling microscopy images with a series of Monte Carlo simulations

A systematic computational study of the structure crossover and coordination number distribution of metallic nanoparticles

In this study, we identified stable configurations for three nanoparticle structure motifs (icosahedral, decahedral and cuboctahedral) of eight transition metals (Cu, Ag, Au, Pd, Ni, Rh, Ir, and Pt) ranging in size from 140 to 3000 atoms. We made simple yet precise analytical approximations of the energy of the stable configurations as a function of nanoparticle size and calculated the structure crossover sizes from these approximations. We then analyzed the surface structure of the nanoparticles in terms of the distribution of the coordination numbers and active sites. We found that low-coordinated atoms are most preferable for cuboctahedral forms and for lighter metals – Cu, Ni and Rh. Compared to other considered metals, gold nanoparticles exhibited unique features as follows: the least amount of low-coordinated atoms, the largest fraction of (111) faces on its surface and a concave reconstruction of five-fold vertices