Development of a Microkinetic Model for the CO2 Methanation with an Automated Reaction Mechanism Generator. 30th European Symposium on Computer Aided Process Engineering. Elsevier; 2020. pp. 529-34. [DOI: 10.1016/b978-0-12-823377-1.50089-6]

Linking Experimental and Ab Initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy

Enthalpies of formation of adsorbates are crucial parameters in the microkinetic modeling of heterogeneously catalyzed reactions since they quantify the stability of intermediates on the catalyst surface. This quantity is often computed using density functional theory (DFT), as more accurate methods are computationally still too expensive, which means that the derived enthalpies have a large uncertainty. In this study, we propose a new error cancellation method to compute the enthalpies of formation of adsorbates from DFT more accurately through a generalized connectivity-based hierarchy. The enthalpy of formation is determined through a hypothetical reaction that preserves atomistic and bonding environments. The method is applied to a data set of 60 adsorbates on Pt(111) with up to 4 heavy (non-hydrogen) atoms. Enthalpies of formation of the fragments required for the bond balancing reactions are based on experimental heats of adsorption for Pt(111). The comparison of enthalpies of formation derived from different DFT functionals using the isodesmic reactions shows that the effect of the functional is significantly reduced due to the error cancellation. Thus, the proposed methodology creates an interconnected thermochemical network of adsorbates that combines experimental with ab initio thermochemistry in a single and more accurate thermophysical database.

Combustion, Chemistry, and Carbon Neutrality

Combustion is a reactive oxidation process that releases energy bound in chemical compounds used as fuels─energy that is needed for power generation, transportation, heating, and industrial purposes. Because of greenhouse gas and local pollutant emissions associated with fossil fuels, combustion science and applications are challenged to abandon conventional pathways and to adapt toward the demand of future carbon neutrality. For the design of efficient, low-emission processes, understanding the details of the relevant chemical transformations is essential. Comprehensive knowledge gained from decades of fossil-fuel combustion research includes general principles for establishing and validating reaction mechanisms and process models, relying on both theory and experiments with a suite of analytic monitoring and sensing techniques. Such knowledge can be advantageously applied and extended to configure, analyze, and control new systems using different, nonfossil, potentially zero-carbon fuels. Understanding the impact of combustion and its links with chemistry needs some background. The introduction therefore combines information on exemplary cultural and technological achievements using combustion and on nature and effects of combustion emissions. Subsequently, the methodology of combustion chemistry research is described. A major part is devoted to fuels, followed by a discussion of selected combustion applications, illustrating the chemical information needed for the future.

ChemDyME: Kinetically Steered, Automated Mechanism Generation through Combined Molecular Dynamics and Master Equation Calculations

In many scientific fields, there is an interest in understanding the way in which chemical networks evolve. The chemical networks which researchers focus upon have become increasingly complex, and this has motivated the development of automated methods for exploring chemical reactivity or conformational change in a "black-box" manner, harnessing modern computing resources to automate mechanism discovery. In this work, we present a new approach to automated mechanism generation which couples molecular dynamics and statistical rate theory to automatically find kinetically important reactions and then solve the time evolution of the species in the evolving network. The key to this chemical network mapping through combined dynamics and ME simulation approach is the concept of "kinetic convergence", whereby the search for new reactions is constrained to those species which are kinetically favorable at the conditions of interest. We demonstrate the capability of the new approach for two systems, a well-studied combustion system and a multiple oxygen addition system relevant to atmospheric aerosol formation.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.