VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Achenie, Luke E."

Now showing 1 - 5 of 5
  • Thumbnail Image
    Accelerating Catalytic Materials Discovery for Sustainable Nitrogen Transformations by Interpretable Machine Learning
    Pillai, Hemanth Somarajan (Virginia Tech, 2023-01-12)
    Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within the nitrogen cycle and have important applications within tackling current environmental issues. Mechanisms are studied through the use of density functional theory (DFT) for AOR and NO3RR, subsequently a descriptor based approach is used to understand activity trends on a wide range of electrocatalysts. For AOR inter- pretable machine learning is used in conjunction with active learning to screen for active and stable ternary electrocatalysts. We find Pt3RuCo, Pt3RuNi and Pt3RuFe show great activity, and are further validated via experimental results. By leveraging the advantages of the interpretible machine learning model we elucidate the underlying electronic factors for the stronger *N binding which leads to the observed improved activity. For NO3RR an interpretible machine learning model is used to understand ways to bypass the stringent limitations put on the electrocatalytic activity due to the *N vs *NO3 scaling relations. It is found that the *N binding energy can be tuned while leaving the *NO3 binding energy unaffected by ensuring that the subsurface atom interacts strongly with the *N. Based on this analysis we suggest the B2 CuPd as a potential active electrocatalyst for this reaction, which is further validated by experiments
  • Thumbnail Image
    Advancing computational materials design and model development using data-driven approaches
    Sose, Abhishek Tejrao (Virginia Tech, 2024-02-02)
    Molecular dynamics (MD) simulations find their applications in fundamental understanding of molecular level mechanisms of physical processes. This assists in tuning the key features affecting the development of the novel hybrid materials. A certain application demanding the need for a desired function can be cherished through the hybrids with a blend of new properties by a combination of pure materials. However, to run MD simulations, an accurate representation of the interatomic potentials i.e. force-fields (FF) models remain a crucial aspect. This thesis intricately explores the fusion of MD simulations, uncertainty quantification, and data-driven methodologies to accelerate the computational design of innovative materials and models across the following interconnected chapters. Beginning with the development of force fields for atomic-level systems and coarse-grained models for FCC metals, the study progresses into exploring the intricate interfacial interactions between 2D materials like graphene, MoS2, and water. Current state-of-the-art model development faces the challenge of high dimensional input parameters' model and unknown robustness of developed model. The utilization of advanced optimization techniques such as particle swarm optimization (PSO) integrated with MD enhances the accuracy and precision of FF models. Moreover, the bayesian uncertainty quantification (BUQ) assists FF model development researchers in estimating the robustness of the model. Furthermore, the complex structure and dynamics of water confined between and around sheets was unraveled using 3D Convolutional Neural Networks (3D-CNN). Specifically, through classification and regression models, water molecule ordering/disordering and atomic density profiles were accurately predicted, thereby elucidating nuanced interplays between sheet compositions and confined water molecules. To further the computational design of hybrid materials, this thesis delves into designing and investigating polymer composites with functionalized MOFs shedding light on crucial factors governing their compatibility and performance. Therefore, this report includes the study of structure and dynamics of functionalized MOF in the polymer matrix. Additionally, it investigates the biomedical potential of porous MOFs as drug delivery vehicles (DDVs). Often overlooked is the pivotal role of solvents (used in MOF synthesis or found in relevant body fluids) in the drug adsorption and release process. This report underscores the solvent's impact on drug adsorption within MOFs by comparing results in its presence and absence. Building on these findings, the study delves into the effects of MOF functionalization on tuning the drug adsorption and release process. It further explores how different physical and chemical properties influence drug adsorption within MOFs. Furthermore, the research explores the potential of functionalized MOFs for improved carbon capture, considering their application in energy-related contexts. By harnessing machine learning and deep learning, the thesis introduces innovative pathways for material property prediction and design, emphasizing the pivotal fusion of computational methodologies with data-driven approaches to advance molecular-level understanding and propel future material design endeavors.
  • Thumbnail Image
    Computational and Data-Driven Design of Perturbed Metal Sites for Catalytic Transformations
    Huang, Yang (Virginia Tech, 2024-05-23)
    We integrate theoretical, computational and data-driven approaches for the sake of understanding, design and discovery of metal based catalysts. Firstly, we develop theoretical frameworks for predicting electronic descriptors of transition and noble metal alloys, including a physics model of d-band center, and a tight-binding theory of d-band moments to systematically elucidate the distinct electronic structures of novel catalysts. Within this framework, the hybridization of semi-empirical theories with graph neural network and attribution analysis enables accurate prediction equipped with mechanistic insights. In addition, novel physics effect controlling surface reactivity beyond conventional understanding is uncovered. Secondly, we develop a computational and data-driven framework to model high entropy alloy (HEA) catalysis, incorporating thermodynamic descriptor-based phase stability evaluation, surface segregation modeling by deep learning potential-driven molecular simulation and activity prediction through machine learning-embedded electrokinetic model. With this framework, we successfully elucidate the experimentally observed improved activity of PtPdCuNiCo HEA in oxygen reduction reaction. Thirdly, a Bayesian optimization framework is employed to optimize racemic lactide polymerization by searching for stereoselective aluminum (Al) -complex catalysts. We identified multiple new Al-complex molecules that catalyzed either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovered mechanistically meaningful ligand descriptors that can access quantitative and predictive models for catalyst development.
  • Thumbnail Image
    Toward Designing Active ORR Catalysts via Interpretable and Explainable Machine Learning
    Omidvar, Noushin (Virginia Tech, 2022-09-22)
    The electrochemical oxygen reduction reaction (ORR) is a very important catalytic process that is directly used in carbon-free energy systems like fuel cells. However, the lack of active, stable, and cost-effective ORR cathode materials has been a major impediment to the broad adoption of these technologies. So, the challenge for researchers in catalysis is to find catalysts that are electrochemically efficient to drive the reaction, made of earth-abundant elements to lower material costs and allow scalability, and stable to make them last longer. The majority of commercial catalysts that are now being used have been found through trial and error techniques that rely on the chemical intuition of experts. This method of empirical discovery is, however, very challenging, slow, and complicated because the performance of the catalyst depends on a myriad of factors. Researchers have recently turned to machine learning (ML) to find and design heterogeneous catalysts faster with emerging catalysis databases. Black-box models make up a lot of the ML models that are used in the field to predict the properties of catalysts that are important to their performance, such as their adsorption energies to reaction intermediates. However, as these black-box models are based on very complicated mathematical formulas, it is very hard to figure out how they work and the underlying physics of the desired catalyst properties remains hidden. As a way to open up these black boxes and make them easier to understand, more attention is being paid to interpretable and explainable ML. This work aims to speed up the process of screening and optimizing Pt monolayer alloys for ORR while gaining physical insights. We use a theory-infused machine learning framework in combination with a high-throughput active screening approach to effectively find promising ORR Pt monolayer catalysts. Furthermore, an explainability game-theory approach is employed to find electronic factors that control surface reactivity. The novel insights in this study can provide new design strategies that could shape the paradigm of catalyst discovery.
  • Thumbnail Image
    Understanding Interfacial Kinetics of Catalytic Carbon Dioxide Transformations from Multiscale Simulations
    Mou, Tianyou (Virginia Tech, 2023-07-19)
    Carbon dioxide (CO2), as a greenhouse gas, has shown to achieve the highest level in history, causes the global warming issue, leading to a 1.2 ℃ increase of the global average temperature. The consumption of fossil fuels is one of the main reasons that cause CO2 emission. Current industrial production of chemicals accounts for 29% of total fossil fuels consumption, which can be the feedstock or raw materials for carbon source, or act as the fuel to generate heat and power. CO2 conversion technologies, e.g., thermo-catalytic reaction and electrochemical reduction, have drawn researchers' attention, since they have the potential to resolve the feedstock and fuel consumption sectors of chemical production at the same time. CO2 conversion technologies use CO2 as the direct carbon source of chemicals and store the intermittent renewable energies as the energy source, which can ultimately achieve a net-zero CO2 emission and produce value-added chemical products. However, there are challenges for a practical application of CO2 conversion technologies. For instance, electrochemical CO2 reduction reaction (ECO2RR) suffers from the low activity and selectivity, while thermocatalytic CO2 conversion, or the CO2 hydrogenation reaction, usually requires harsh reaction conditions and has a low selectivity. Nonetheless, the improvement of developing new promising catalysts remains limited, due to the lack of insights of the reactions. The complex reaction networks and kinetics lead to an elusive reaction mechanism, and various effects, e.g., solvation, potential, structure, and coverage, hinder our fundamental understanding of catalytic processes. Herein, we report the efforts that we have been put in to gain insights of reaction mechanism of CO2 reduction reactions. Bi has shown to reduce CO2 to formic acid (HCOOH), while we have found that, by constituting a Bi-Cu2S heterostructure catalyst, a better catalytic performance was achieved, due to the structural effect of the interface (Chapter 2). However, it is shown that the CO2 electrochemical reduction mechanism on Bi has changed when switching the electrolyte from water to aprotic media, e.g., ionic liquids, and CO was obtained as the main product instead of HCOOH, showing a shift of reaction pathway due to the electrolyte effect (Chapter 3). However, the fundamental understanding of reaction mechanism requires not only the reaction pathways, but the reaction kinetics under reaction conditions, where the lateral or adsorbate-adsorbate interactions play an important role. In this case, we summarized recent advances of applications of machine learning (ML) algorithms for adsorbate-adsorbate interaction model developments to deal with the realistic reaction kinetics (Chapter 4). The lattice based Kinetic Monte Carlo (KMC) has shown promising performances for considering the lateral interactions of surface reactions. We report the mechanistic and KMC kinetic study of CO2 hydrogenation on Cesium promoted Au(111) surface, to gain insights of alkali metal promoting effects under reaction conditions (Chapter 5). To expand the scope, the integration of CO2 reduction with the C-N bond formation provides a promising strategy to produce more value-added product such as urea. Recent studies show that urea can be produced by reducing CO2 and nitrate (NO3-) from wastewater, which mitigate both global warming and nitrate pollution issue. However, the reaction mechanism remains elusive due to the complicated reaction network. Therefore, we employed the first-principles molecular dynamics to reveal the reaction mechanism of C-N coupling and the effect of different reaction conditions including applied potential and electrolyte (Chapter 6). Although recent advances in the computational catalysis field have significantly push forward the understanding of the chemistry nature of heterogeneous catalysis, the gap between theory and experiment remains far beyond bridged due to the complexity nature of the problem in a wide range of time and length scales, hinders the development and discovery of active catalytic materials. Recent advances of narrowing and bridging the complexity gap between theory and experiment with machine learning have been summarized to emphasize the importance of utilizing machine learning for rational catalyst design (Chapter 7).