kmos: A lattice kinetic Monte Carlo framework

Kinetic Monte Carlo (kMC) simulations have emerged as a key tool for microkinetic modeling in heterogeneous catalysis and other materials applications. Systems, where site-specificity of all elementary reactions allows a mapping onto a lattice of discrete active sites, can be addressed within the particularly efficient lattice kMC approach. To this end we describe the versatile kmos software package, which offers a most user-friendly implementation, execution, and evaluation of lattice kMC models of arbitrary complexity in one- to three-dimensional lattice systems, involving multiple active sites in periodic or aperiodic arrangements, as well as site-resolved pairwise and higher-order lateral interactions. Conceptually, kmos achieves a maximum runtime performance which is essentially independent of lattice size by generating code for the efficiency-determining local update of available events that is optimized for a defined kMC model. For this model definition and the control of all runtime and evaluation aspects kmos offers a high-level application programming interface. Usage proceeds interactively, via scripts, or a graphical user interface, which visualizes the model geometry, the lattice occupations and rates of selected elementary reactions, while allowing on-the-fly changes of simulation parameters. We demonstrate the performance and scaling of kmos with the application to kMC models for surface catalytic processes, where for given operation conditions (temperature and partial pressures of all reactants) central simulation outcomes are catalytic activity and selectivities, surface composition, and mechanistic insight into the occurrence of individual elementary processes in the reaction network.Comment: 21 pages, 12 figure

A Combined Molecular Dynamics and Density Functional Theory Approach for Generating Liquid Water Configurations for Aqueous-Phase Heterogeneous Catalysis Studies

Aqueous-phase heterogeneous catalysis is an important chemical process in applications such as water remediation, fuel cells, and the production of fuels and chemicals, including from biomass sources. However, designing alternative, improved catalyst materials for these applications is difficult due to fluctuations in the solvation environment surrounding the catalytic species. In order to elucidate the thermodynamics and kinetics of the relevant reactions, it is imperative to gain a better understanding of the roles of liquid water molecules in these reactions. In this work, a method combining both classical and quantum simulations was developed to generate configurations of liquid water molecules over catalytic species adsorbed on a catalyst surface, which can provide valuable insight into the roles of the liquid water reaction environment on aqueous-phase heterogeneous catalysis. The method developed in this work entails combining force field molecular dynamics (FFMD) and density functional theory (DFT) simulations. This method leverages the strengths of each type of simulation to enable the calculation of catalytic energies under “realistic” liquid water configurations. FFMD simulations are used to generate trajectories of liquid water configurations that include thermal fluctuations, while DFT simulations are used to capture the energies associated with bond breaking and forming that are required for microkinetic modeling and catalyst design studies. This FFMD-DFT method was used to calculate the interaction energies between the liquid water environment and the reaction intermediate or transition state species. The trend in the calculated interaction energies was shown to correlate with the trend in hydrogen-bond formation between liquid water molecules and the catalytic species. This work also demonstrated that entropic effects due to the thermal fluctuations in the solvation environment are a significant contribution to the free energies calculated for aqueous-phase, heterogeneously-catalyzed systems. The FFMD-DFT method was also used to calculate reaction energies, activation barriers, and pre-exponential factors to study the kinetics of example O—H and C—H cleavage reactions on a platinum catalyst surface under an aqueous reaction environment. Using this method, it was found that O—H cleavage reactions prefer H2O-mediated pathways, while C—H cleavage reactions prefer non-H2O-mediated pathways. In summary, the FFMD-DFT method developed in this work has been shown to be a robust technique for generating realistic liquid water configurations over catalytic species on a platinum catalyst surface. Those liquid water configurations can be used to calculate catalytic properties that can provide insight into the roles of water molecules in these reactions and facilitate microkinetic modeling and catalyst design studies

SimStack: An Intuitive Workflow Framework

Establishing a fundamental understanding of the nature of materials via computational simulation approaches requires knowledge from different areas, including physics, materials science, chemistry, mechanical engineering, mathematics, and computer science. Accurate modeling of the characteristics of a particular system usually involves multiple scales and therefore requires the combination of methods from various fields into custom-tailored simulation workflows. The typical approach to developing patch-work solutions on a case-to-case basis requires extensive expertise in scripting, command-line execution, and knowledge of all methods and tools involved for data preparation, data transfer between modules, module execution, and analysis. Therefore multiscale simulations involving state-of-the-art methods suffer from limited scalability, reproducibility, and flexibility. In this work, we present the workflow framework SimStack that enables rapid prototyping of simulation workflows involving modules from various sources. In this platform, multiscale- and multimodule workflows for execution on remote computational resources are crafted via drag and drop, minimizing the required expertise and effort for workflow setup. By hiding the complexity of high-performance computations on remote resources and maximizing reproducibility, SimStack enables users from academia and industry to combine cutting-edge models into custom-tailored, scalable simulation solutions

Multiscale QM/MM modelling of catalytic systems with ChemShell

Hybrid quantum mechanical/molecular mechanical (QM/MM) methods are a powerful computational tool for the investigation of all forms of catalysis, as they allow for an accurate description of reactions occurring at catalytic sites in the context of a complicated electrostatic environment. The scriptable computational chemistry environment ChemShell is a leading software package for QM/MM calculations, providing a flexible, high performance framework for modelling both biomolecular and materials catalysis. We present an overview of recent applications of ChemShell to problems in catalysis and review new functionality introduced into the redeveloped Python-based version of ChemShell to support catalytic modelling. These include a fully guided workflow for biomolecular QM/MM modelling, starting from an experimental structure, a periodic QM/MM embedding scheme to support modelling of metallic materials, and a comprehensive set of tutorials for biomolecular and materials modelling

Interfacing CRYSTAL/AMBER to Optimize QM/MM Lennard–Jones Parameters for Water and to Study Solvation of TiO2 Nanoparticles

Publisher's version (útgefin grein)Metal oxide nanoparticles (NPs) are regarded as good candidates for many technological applications, where their functional environment is often an aqueous solution. The correct description of metal oxide electronic structure is still a challenge for local and semilocal density functionals, whereas hybrid functional methods provide an improved description, and local atomic function-based codes such as CRYSTAL17 outperform plane wave codes when it comes to hybrid functional calculations. However, the computational cost of hybrids are still prohibitive for systems of real sizes, in a real environment. Therefore, we here present and critically assess the accuracy of our electrostatic embedding quantum mechanical/molecular mechanical (QM/MM) coupling between CRYSTAL17 and AMBER16, and demonstrate some of its capabilities via the case study of TiO2 NPs in water. First, we produced new Lennard–Jones (LJ) parameters that improve the accuracy of water–water interactions in the B3LYP/TIP3P coupling. We found that optimizing LJ parameters based on water tri- to deca-mer clusters provides a less overstructured QM/MM liquid water description than when fitting LJ parameters only based on the water dimer. Then, we applied our QM/MM coupling methodology to describe the interaction of a 1 nm wide multilayer of water surrounding a spherical TiO2 nanoparticle (NP). Optimizing the QM/MM water–water parameters was found to have little to no effect on the local NP properties, which provide insights into the range of influence that can be attributed to the LJ term in the QM/MM coupling. The effect of adding additional water in an MM fashion on the geometry optimized nanoparticle structure is small, but more evident effects are seen in its electronic properties. We also show that there is good transferability of existing QM/MM LJ parameters for organic molecules–water interactions to our QM/MM implementation, even though these parameters were obtained with a different QM code and QM/MM implementation, but with the same functional.National Council for Eurasian and East European Research. Funding: This research was funded by the Icelandic Research Fund (grant 174244-051) and VILLUM FONDEN, the European Research Council (ERC) under the European Union’s HORIZON2020 research and innovation programme (ERC Grant Agreement No [647020]). Acknowledgments: A.O.D. Would like to thank Jónsson, H. for discussions about fitting strategies. C.D.V. is grateful to Lara Ferrighi, Massimo Olivucci, and Stefano Motta for fruitful discussions. A.O.D. Acknowledges funding from the Icelandic Research Fund (grant 174244-051) and VILLUM FONDEN. The project has received funding from the European Research Council (ERC) under the European Union’s HORIZON2020 research and innovation programme (ERC Grant Agreement No [647020]).Peer Reviewe

matscipy : materials science at the atomic scale with Python

Behaviour of materials is governed by physical phenomena that occur at an extreme range of length and time scales. Computational modelling requires multiscale approaches. Simulation techniques operating on the atomic scale serve as a foundation for such approaches, providing necessary parameters for upper-scale models. The physical models employed for atomic simulations can vary from electronic structure calculations to empirical force fields. However, construction, manipulation and analysis of atomic systems are independent of the given physical model but dependent on the specific application. matscipy implements such tools for applications in materials science, including fracture, plasticity, tribology and electrochemistry

Exploring Functional Photonic Devices made from a Chiral Metal-Organic Framework Material by a Multiscale Computational Method

Electronic circular dichroism is an important optical phenomenon offering insights into chiral molecular materials. On the other hand, metal-organic frameworks (MOFs) are a novel group of crystalline porous thin-film materials that provide tailor-made chemical and physical properties by carefully selecting their building units. Combining these two aspects of contemporary material research and integrating chiral molecules into MOFs promises devices with unprecedented functionality. However, considering the nearly unlimited degrees of freedom concerning the choice of materials and the geometrical details of the possibly structured films, we urgently need to complement advanced experimental methods with equally strong modeling techniques. Most notably, these modeling techniques must cope with the challenge that the material and devices thereof cover size scales from {\AA}ngstr\"oms to mm. In response to that need, we outline a computational workflow that seamlessly combines quantum chemical methods to capture the properties of individual molecules with optical simulations to capture the properties of functional devices made from these molecular materials. We concentrate on chiral properties and apply our work to UiO-67-BINOL MOFs, for which experimental results are available to benchmark the results of our simulations and explore the optical properties of cavities and metasurfaces made from that chiral material