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Open Access Publications from the University of California

Scholarly Works (7 results)

  • Thesis
  • Peer Reviewed
UC Riverside Electronic Theses and Dissertations (2022)

The real-time time-dependent density functional theory (rt-TDDFT) approach, which is complementary to the more traditional linear-response TDDFT (lr-TDDFT), propagates the electron density in real time for studying the ground and excited states. Researchers use rt-TDDFT to study electron dynamics in real time. Moreover, rt-TDDFT can treat nonlinear effects, which is especially useful for experimentalists studying external field effects in complex systems. This dissertation presents computational methods for studying a complex system's ground and excited states. We start with ground state calculations, via density functional theory (DFT), for studies on Cyclodextrins as a catalyst, binary compound convex hull, and transition states. Then we go into ground state studies with nuclear motion using the Born-Oppenheimer Molecular Dynamics. An application of lr-TDDFT on TiSe$_{2}$ follows this. We transition to excited state studies by introducing our rt-TDDFT formalism. We validate the implementation with benchmark tests for essential elements. Finally, we demonstrate our implementation capabilities and apply them to practical systems. The first application simulates the attosecond transient absorption spectroscopy for charge transfer and polarization switching in BaTiO$_{3}$. The second application involves molecular dynamics, a nonlinear process, for photo-induced degradation mechanisms of perfluorooctanoic acid (PFOA). By explicitly accounting for non-adiabatic excited-state interactions in solvated PFOA, we show that these photo-induced excitations enable a charge-transfer process that polarizes the C\textendash F bond, resulting in a dynamic dissociation on a femtosecond time scale. Ultimately, this dissertation emphasizes the importance of quantum simulations for studying excited states of molecular and extended systems.

    Cover page: Advanced Computational Methods for Ground and Excited States to Study Complex SystemsCreative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    UC Riverside Previously Published Works (2023)
    Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.
      Cover page: Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs).
      • Article
      • Peer Reviewed
      UC Riverside Previously Published Works (2023)
      The deleterious impact of erosion due to high-velocity particle impingement adversely affects a variety of engineering and industrial systems, resulting in irreversible mechanical wear of materials/components. Brute force computational fluid dynamics (CFD) calculations are commonly used to predict surface erosion by directly solving the Navier-Stokes equations for fluid and particle dynamics; however, these numerical approaches often require significant computational resources. In contrast, recent data-driven approaches using machine learning (ML) have shown immense promise for more efficient and accurate predictions to sidestep computationally demanding CFD calculations. To this end, we have developed FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer), a new hybrid ML architecture for accurately predicting particle trajectories and erosion on an industrial-scale steam header geometry. Our FLUID-GPT approach utilizes a Generative Pre-Trained Transformer 2 (GPT-2) with a convolutional neural network (CNN) for the first time to predict surface erosion using only information from five initial conditions: particle size, main-inlet speed, main-inlet pressure, subinlet speed, and subinlet pressure. Compared to the bidirectional long- and short-term memory (BiLSTM) ML techniques used in previous work, our FLUID-GPT model is much more accurate (a 54% decrease in the mean squared error) and efficient (70% less training time). Our work demonstrates that FLUID-GPT is an accurate and efficient ML approach for predicting time-series trajectories and their subsequent spatial erosion patterns in these complex dynamic systems.
        Cover page: FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer): Efficient Predictions of Particle Trajectories and Erosion.
        • Article
        • Peer Reviewed
        UC Riverside Previously Published Works (2018)
        In a recent communication, Weber and co-workers presented a surprising study on charge-localization effects in the N,N'-dimethylpiperazine (DMP+) diamine cation to provide a stringent test of density functional theory (DFT) methods. Within their study, the authors examined various DFT methods and concluded that "all DFT functionals commonly used today, including hybrid functionals with exact exchange, fail to predict a stable charge-localized state." This surprising conclusion is based on the authors' use of a self-interaction correction (namely, complex-valued Perdew-Zunger Self-Interaction Correction (PZ-SIC)) to DFT, which appears to give excellent agreement with experiment and other wavefunction-based benchmarks. Since the publication of this recent communication, the same DMP+ molecule has been cited in numerous subsequent studies as a prototypical example of the importance of self-interaction corrections for accurately calculating other chemical systems. In this correspondence, we have carried out new high-level CCSD(T) analyses on the DMP+ cation to show that DFT actually performs quite well for this system (in contrast to their conclusion that all DFT functionals fail), whereas the PZ-SIC approach used by Weber et al. is the outlier that is inconsistent with the high-level CCSD(T) (coupled-cluster with single and double excitations and perturbative triples) calculations. Our new findings and analysis for this system are briefly discussed in this correspondence.
          Cover page: The diamine cation is not a chemical example where density functional theory fails
          • Article
          • Peer Reviewed
          UC Riverside Previously Published Works (2018)
          We performed a computational investigation of the mechanism by which cyclodextrins (CDs) catalyze Diels-Alder reactions between 9-anthracenemethanol and N-cyclohexylmaleimide. Hydrogen bonds (Hbonds) between N-cyclohexylmaleimide and the hydroxyl groups of cyclodextrins were suggested to play an important role in this catalytic process. However, our free energy calculations and molecular dynamics simulations showed that these Hbonds are not stable, and quantum mechanical calculations suggested that the reaction is not promoted by these Hbonds. The binding of 9-anthracenemethanol and N-cyclohexylmaleimide to cyclodextrins was the key to the catalytic process. Cyclodextrins act as a container to hold the two reactants in the cavity, pre-organize them for the reactions, and thus reduce the entropy penalty to the activation free energy. Dimethyl-β-CD was a better catalyst for this specific reaction than β-CD because of its stronger van der Waals interaction with the pre-organized reactants and its better performance in reducing the activation energy. This computational work sheds light on the mechanism of the catalytic reaction by cyclodextrins and introduces new perspectives of supramolecular catalysis.
            Cover page: An MM and QM Study of Biomimetic Catalysis of Diels-Alder Reactions Using Cyclodextrins.
            • Article
            • Peer Reviewed
            UC Riverside Previously Published Works (2023)
            We present a new implementation of real-time time-dependent density functional theory (RT-TDDFT) for calculating excited-state dynamics of periodic systems in the open-source Python-based PySCF software package. Our implementation uses Gaussian basis functions in a velocity gauge formalism and can be applied to periodic surfaces, condensed-phase, and molecular systems. As representative benchmark applications, we present optical absorption calculations of various molecular and bulk systems and a real-time simulation of field-induced dynamics of a (ZnO)4 molecular cluster on a periodic graphene sheet. We present representative calculations on optical response of solids to infinitesimal external fields as well as real-time charge-transfer dynamics induced by strong pulsed laser fields. Due to the widespread use of the Python language, our RT-TDDFT implementation can be easily modified and provides a new capability in the PySCF code for real-time excited-state calculations of chemical and material systems.
              Cover page: Implementation of real‐time TDDFT for periodic systems in the open‐source PySCF software packageCreative Commons 'BY-NC-ND' version 4.0 license
              • Article
              • Peer Reviewed
              UC Riverside Previously Published Works (2018)
              Anisotropic magnetoresistance (AMR) of Cr2Ge2Te6 (CGT), a layered ferromagnetic insulator, is investigated under an applied hydrostatic pressure up to 2 GPa. The easy axis direction of the magnetization is inferred from the AMR saturation feature in the presence and absence of the applied pressure. At zero applied pressure, the easy axis is along the c-direction or perpendicular to the layer. Upon application of a hydrostatic pressure>1 GPa, the uniaxial anisotropy switches to easy-plane anisotropy which drives the equilibrium magnetization from the c-axis to the ab-plane at zero magnetic field, which amounts to a giant magnetic anisotropy energy change (>100%). As the temperature is increased across the Curie temperature, the characteristic AMR effect gradually decreases and disappears. Our first-principles calculations confirm the giant magnetic anisotropy energy change with moderate pressure and assign its origin to the increased off-site spin-orbit interaction of Te atoms due to a shorter Cr-Te distance. Such a pressure-induced spin reorientation transition is very rare in three-dimensional ferromagnets, but it may be common to other layered ferromagnets with similar crystal structures to CGT, and therefore offers a unique way to control magnetic anisotropy.
                Cover page: Pressure-induced spin reorientation transition in layered ferromagnetic insulator Cr2Ge2Te6
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