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Open Access Publications from the University of California

Scholarly Works (4 results)

  • Thesis
  • Peer Reviewed
UC Davis Electronic Theses and Dissertations (2023)

In this era driven by data science and artificial intelligence, traditional computational catalysis has undergone a profound revolution facilitated by a variety of machine learning and deep learning techniques. For the new generation of computational theorists and experts in the field of catalysis and surface science, it is crucial to grasp how data-driven models and workflows can seamlessly integrate into scientific research domain. This dissertation explores two primary categories of studies in modern catalysis informatics. Type I studies are centered around predicting specific physical properties related to and playpivotal roles in governing various aspects of catalyst performance, including reactivity, selectivity, and activity. Type II studies, on the other hand, focus on the development of Machine-learning Potentials (Force Fields). These are meticulously engineered to extend the applicability of computationally intensive ab-initio methods (density functional theory, conventional molecular dynamics, and metadynamics) to larger catalytic systems and longer simulation timeframes for enhanced transferability and scalability. The overarching objective of both studies is to extract valuable structural, thermodynamic, and kinetic insights from catalytic systems that were previously inaccessible or inefficient using traditional simulation methods. This dissertation is organized into 5 chapters. Chapter 1 serves as an extensive review, akin to a concise textbook, covering data-driven research methods in catalysis informatics. It places particular emphasis on multiscale modeling techniques, descriptor (fingerprint) design for complex molecular systems, the foundational theory and typical algorithms of machine learning and deep learning, and the workflow for developing Machine-learning Potentials. Chapters 2 - 4 comprise the papers where I served as the first (or co-first) author, delving into accelerating the research progress in catalysis informatics. The final chapter 5 offers a summary and outlines potential avenues for future research in the field of catalysis informatics.

    • Article
    • Peer Reviewed
    UC Davis Previously Published Works (2024)
    This work aims to address the challenge of developing interpretable ML-based models when access to large-scale computational resources is limited. Using CoMoFeNiCu high-entropy alloy catalysts as an example, we present a cost-effective workflow that synergistically combines descriptor-based approaches, machine learning-based force fields, and low-cost density functional theory (DFT) calculations to predict high-quality adsorption energies for H, N, and NHx (x = 1, 2, and 3) adsorbates. This is achieved using three specific modifications to typical DFT workflows including: (1) using a sequential optimization protocol, (2) developing a new geometry-based descriptor, and (3) repurposing the already-available low-cost DFT optimization trajectories to develop a ML-FF. Taken together, this study illustrates how cost-effective DFT calculations and appropriately designed descriptors can be used to develop cheap but useful models for predicting high-quality adsorption energies at significantly lower computational costs. We anticipate that this resource-efficient philosophy may be broadly relevant to the larger surface catalysis community.
      Cover page: Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts
      • Article
      • Peer Reviewed
      UC Davis Previously Published Works (2024)
      Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.
        Cover page: Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training.
        • Article
        • Peer Reviewed
        UC Davis Previously Published Works (2024)
        Nanoengineered metal@zeolite materials have recently emerged as a promising class of catalysts for several industrially relevant reactions. These materials, which consist of small transition metal nanoclusters confined within three-dimensional zeolite pores, are interesting because they show higher stability and better sintering resistance under reaction conditions. While several such hybrid catalysts have been reported experimentally, key questions such as the impact of the zeolite frameworks on the properties of the metal clusters are not well understood. To address such knowledge gaps, in this study, we report a robust and transferable machine learning-based potential (MLP) that is capable of describing the structure, stability, and dynamics of zeolite-confined gold nanoclusters. Specifically, we show that the resulting MLP maintains ab initio accuracy across a range of temperatures (300-1000 K) and can be used to investigate time scales (>10 ns), length scales (ca. 10,000 atoms), and phenomena (e.g., ensemble-averaged stability and diffusivity) that are typically inaccessible using density functional theory (DFT). Taken together, this study represents an important step in enabling the rational theory-guided design of metal@zeolite catalysts.
          Cover page: Stability and Dynamics of Zeolite-Confined Gold Nanoclusters.
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