INVESTIGATION OF MORPHOLOGY AND FUNCTIONALITY OF MULTI-COMPONENT CATALYST USING FIRST-PRINCIPLES AND MACHINE-LEARNING
Throughout history, emergent technologies have become possible due to discovery, synthesis, and access to improved materials. Heterogeneous catalysts, which are the bedrock of the modern chemical industry, are no exception. Advances in catalyst research have resulted in wide-ranging applications from energy production and storage, to the synthesis of fine chemicals and drug discovery. Now, more than ever, discovering next-generation catalysts is crucial as we work towards a carbon-neutral and sustainable economy.
That said, heterogeneous catalytic reactions are shown to be sensitive to the atomic-scale complexities arising under in-operando conditions, such as variations in surface morphology, composition, and adsorbate-adsorbate interactions. To understand the subtle interplay of these diverse phenomena, it is necessary to develop an atomic-scale representation of the catalyst under the reaction conditions. In that spirit, understanding what makes the catalyst “active” and stable is vital to tease out the insights, and develop principles that lay the groundwork for material discovery. Understanding the molecular-level behavior of materials has been the focus of my doctoral research. Through a combination of atomic-scale simulations, machine learning, spectroscopy, and chemical kinetics we have investigated the active sites and reaction mechanism for reactions relevant for energy generation.
First, water-gas shift reaction on complex metal/oxide interfaces to probe the effect of adsorbate and multi-component interfaces on the reaction, and second, low-temperature NOX decomposition on atomically dispersed metal/oxide catalysts for developing vehicular exhaust pollution abatement protocols. Through this work, we proposed an improved understanding of the catalyst active site and the atomic-scale reaction mechanism.
Further, the presence of numerous atomic-scale configurations, and the difficulty in systematically generating and analyzing the surface representations, make atomic-model development non-trivial.To address this challenge, we proposed two computation tools: 1) A grand-canonical genetic algorithm for structure prediction (GASP) to build multi-component interfacial lattice structures. Through GASP, we can generate catalyst models that consider atomic-scale transformation and metal-support interaction. 2) A graph network-based enumeration scheme and prediction strategy is explored. We discuss the Adsorbate Chemical-Environment based Graph Convolution Neural Network (ACE-GCN), a versatile framework with the ability to encode atomic configurations comprising diverse adsorbates, binding locations, coordination environments, and variations in the substrate morphologies. This workflow is used to generate and rank surface adsorbate configurations for reactions which are shown to be affected by the presence of high adsorbate surface coverage.
The atomic-scale catalyst models and computation tools proposed through this work can serve as a starting point for developing a detailed description of complex catalyst surfaces under in-operando conditions, ultimately leading to fundamental insights into the factors that govern the functioning of heterogeneous catalysis in chemically complex environments.