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-operandoconditions, 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 NOXdecomposition 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-operandoconditions, ultimately leading to fundamental insights into the factors that govern the functioning of heterogeneous catalysis in chemically complex environments

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of surface atomic configurations. To address this challenge, we present the Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that can account for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combine with the low symmetry of the alloy substrate to produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, wherein the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions result in the configurational complexity. In both cases, the ACE-GCN model, having trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully ranks the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions

Active Learning of Ternary Alloy Structures and Energies

High-throughput screening of catalysts using first-principles methods, such as density functional theory (DFT), has traditionally been limited by the large, complex, and multidimensional nature of the associated materials spaces. However, machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of these spaces in a data-efficient manner, typically through active learning-based workflows. In this work, we combine such an active learning scheme with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). Specifically, we train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and utilize the model to make and improve predictions on ternary structures. To do so, we perform reduced optimization over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the manifold learning scheme known as Diffusion Maps. Inspired by statistical mechanics, we derive and apply a dropout-informed acquisition function to select ensembles from which to sample additional structures. During each iteration of our active learning scheme, a representative number of crystals that minimize the acquisition function is selected, their energies are computed with DFT, and our dGCN model is retrained. We demonstrate that both of our reduced optimization techniques can be used to improve predictions of the formation free energy, the target property that determines HEA stability, in the ternary alloy space with a significantly reduced number of costly DFT calculations compared to a high-fidelity model. However, the manner in which these two disparate schemes converge to the target property differs: the physics-based scheme appears akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both active learning schemes can be extended further to incorporate greater number of elements, surface structures, and adsorbate motifs