Amorphous (non-crystalline) materials offer new capabilities and applications relative to their corresponding crystalline phases due to their novel and tunable material properties, including but not limited to larger surface area, wider substrate compatibility, stronger corrosion resistance, and lower temperature processing than their crystal counterparts. However, even the most advanced experimental techniques (e.g., in situ NMR and ``inverse" approaches such as Reverse Monte Carlo (RMC) modeling of diffraction data) cannot directly measure the bulk atomic structure of such complex, non-crystalline materials. This, coupled with the lack of a structural theory of amorphous materials, has limited our understanding about the structure-property relationships required for the optimization of realistic devices. In this thesis, we compare and combine several atomistic modeling techniques for generating accurate and general structures of amorphous systems, both solid and liquid. We develop several metrics for measuring efficiency of configuration space sampling to guide and standardize the computational study of amorphous materials, and use these to differentiate between different samples of amorphous structures. Lastly, we use these structures to help elucidate structure-property relationships in these systems. In amorphous metal oxides, we characterize the types of coordination defects in amorphous metal oxide systems and subsequently identify potential electron and hole traps in their electronic structures. In a liquid system, water-in-salt electrolytes, we evaluate how the hydrogen bonding network and ion coordination shells change with composition, and how these are directly correlated to battery performance.

Atomistic simulations have become a prominent tool in chemistry, physics, and materials science for its capability interpreting experimental measurement, predicting material properties and designing new compounds from atomic-level perspective, for its capability helping visualize the topology of materials and their change during a process or reaction. It has been noted that atomic simulations are providing new data and exciting insights into various phenomenon that cannot be obtained readily in any other way—theory, or experiment. Having a precise image of the structure or the dynamical process at this scale provides a brand-new approach understanding how the properties of a material is determined. The most common simulation approaches are density functional theory (DFT) and molecular dynamic (MD), based on which the research is focusing on discussing mechanisms and properties of certain types of energy storage material by their topology.

This thesis mainly focuses on four project. To explain the enlarged d-spacing of hard carbon comparing to graphite, with classic MD, we simulated the atomic structure of rippled graphene based on the experimental finding that hard carbon is composited by curved graphene platelets. Similarly, from the experiments, a higher battery performance of LiCl battery electrolyte are found at lower temperature with a reason of local environment change at different temperature, which is proven by the atomic topology modeled by classic and AIMD simulations. Despite simply constructing materials, a further utilization of the atomic model is to design new materials by altering their structure. We discussed the possibility of high proton conductivity by analyzing the water hydrogen bonding network in Prussian Blue analogues with graph theory. Another project is built to find flexible organic ligands for Metal-Organic Frameworks (MOFs). It is a time-consuming work to calculate ligands' flexibility by either experimentally synthesizing and characterize or simulate deforming process, which is solved by forecasting the stiffness of molecules from its geometry with Neural Network.

Thermoelectrics (TE) are a class of materials that convert heat directly into electricity. If made sufficiently efficient and inexpensive, these materials could be used to recapture low-grade waste heat from the industrial process as useful electrical energy. The potential energy savings are vast. Recent studies by Lawrence Livermore National Laboratory have reported that more than 68% of U.S. energy consumption escapes as waste heat [1] while recuperating only 10% of heat lost into electricity can improve fuel energy efficiency by 20% [2]. This research presented here pursues strategies to make energy harvesting more efficient by using nanoengineering to improve the energy performance of TEs. Three significant theoretical insights are developed that together create a new design paradigm for engineering both thermal and electrical properties of TEs. The first strategy is to enhance the TE power factor through selectively filtering low-energy electrons. A publicly available python design platform called thermoelectric.py with innovative mathematical approaches to accurately and efficiently compute, from first principles, the strength, and energy dependence of electron scattering from nanoscale pores and particles with different geometries is developed. The second strategy is the design of the nanoscale morphology of porous TEs to detriment the lattice thermal conductivity through phonon coherent effects. An analytical framework is laid out to model heat current anticorrelation (HCAC) in materials containing specific porous topologies. The model predicts that HCAC leads to an extreme reduction in thermal conductivity of up to 80% compared to structures in which the anticorrelation effect is not observed. The third strategy is a large-scale screening of TE alloys containing nanoscale porosity to find the minimum thermal conductivity. A general model is developed to predict lattice thermal conductivity of dielectrics containing nanoscale to macroscale porosity. The model is robust in providing a good approximation of the results from full Boltzmann transport (BTE) simulations of lattice thermal conductivity for a wide range of pores shapes, sizes, and spacings that span both the diffusive and ballistic regimes. This provides a simple yet accurate estimation of thermal transport in nanostructures that can be used to rapidly screen or design materials for a particular thermal task. As such this work provides an important tool to facilitate the design and discovery of materials for thermal-related applications, without explicitly solving the BTE.

Energy storage technologies have developed rapidly in recent years due to their functions in grid regulation, energy transformation, and economic benefits. Among them, lithium-ion battery energy storage has been widely commercialized owing to its advantages including modularized design, rapid response, and high efficiency. The significant problem with lithium batteries at present is safety considerations because of the flammable organic components contained in the electrolyte. In addition, lithium battery energy storage is far from meeting global energy demand in the next few decades.

Aqueous Zinc-ion batteries have been widely studied due to their low toxicity, low cost, and high safety. The advantage of pure metal anodes over intercalated anodes is that they take full advantage of their high theoretical capacity in the metallic state (820mAh/g) and low electrochemical potential (−0.762V vs. SHE). However, the current major challenge for zinc metal anodes is dendrite growth, which potentially causes anode corrosion and further affects the reversibility of the anode.

In this research, we focus on investigating the functions of electrolyte additive - vanillin (C8H8O3) that improves the reversibility of the zinc metal anode. Computation- ally, we use ab initio density functional theory (DFT) calculation to explain the roles that vanillin plays in displacing water from the solvation shell of Zn ions in the electrolyte with and without the effect of the zinc surface. In addition, by integrating the experimental results on the potential vanillin decomposition, we investigate the binding energy preferences and charge transfer among electrolyte molecules with various vanillin derivatives. Furthermore, through molecular dynamics (MD) simulation, we explained the self-assembly mechanism with respect to the initial vanillin orientation placement at the zinc metal anode surface. Later, we investigated the Gaussian Process Regression (GPR) informed Bayesian Inference method to accelerate the exploration of the self-assembly process of finding the global minimum energy configuration without consuming additional computing resources by cooperating with the auxiliary force results.

In summary, the experimental results from our collaboration group show a boost in Coulombic efficiency from 95.38% to 99.34% by the electrolyte additive vanillin. Computationally we demonstrated that there exists a strong driving force for vanillin to replace one water molecule of zinc solvation shell in the highly concentrated electrolyte. In addition, the product of vanillin decomposition H-C8H8O3-Cl binds more strongly to the zinc metal anode than vanillin itself which further reveals that H-C8H8O3-Cl molecules create a passivation layer inhibiting hydrogen evolution reaction. Last but not least, the established Gaussian Process Regression approach provides landscape scanning at the early stage to inform the potential wells’ position so that the following downhill search optimization can be accelerated, and to make the informed decision on the initial structure configuration inputted into traditional DFT and/or MD relaxation to avoid performing many costly minimization calculations.

Amorphous materials have a wide variety of applications and their mechanical, optical, electronic and magnetic properties hold great promise towards current technologies. Just like in crystalline materials, the bulk properties of amorphous materials are often dictated by defects and other structural anomalies or outliers. However, unlike crystalline structures, amorphous materials lack long range order and the disordered nature of their atomic arrangements poses major challenges toward building a quantitative correlation between their local atomic environments, leading to complications in characterizing the structure and relaxation in these materials. This dissertation is composed of three separate studies that together build a methodology for describing and understanding the role of defects in amorphous materials. In one of the projects, the structures of the defects are all well-defined but their mechanical response is unknown and is the focus of our study. On the other hand, the other two studies involve understanding the structure of non-crystalline materials. In one of the studies, the challenge is in characterizing the average structure, its relationship to viscosity, characterizing thermodynamically-driven changes in structures, and quantifying the degree of amorphousness. In a final study, we identify the outliers in amorphous materials, we use metrics of structure and then we find the corresponding mechanical behaviour and response of these outliers. These three combined can establish relationship between defects, defects’ structure and their properties in materials. In this thesis, firstly, we investigate the phenomenon of fragility in amorphous liquids, which characterizes the dynamics of amorphous glass-liquid and refers to the sensitivity of liquid’s bonding network and is associated with non-Arrhenius dependence of the liquid’s viscosity. We demonstrate how fragility in amorphous liquids correlates with atomic size-mismatch which is an important consideration in the design and structural performance of glass forming alloys. Secondly, new computational methods are put forward to explain the numerical values of some useful amorphous configurations to achieve a greater understanding of the relationship between their processing, structure, and properties. Understanding the structure and the weaknesses of amorphous materials is of great importance to determine their physical and mechanical properties and to improve the materials’ design. For this purpose, predictive models were developed to accurately classify and identify the defects in amorphous materials and characterize their structural features. We also illustrate the effect of different cooling rates on these local weaknesses. Lastly, we studied the evolution of the thermal conductivity in pristine LiAlO2 and we elucidated the effects of defects on its thermal properties at different scales which is important to assure its long-term performance and to address these defects appropriately. These contributions are anticipated to enhance our understanding of structure-property relationships of defects in materials, leading to establish useful approaches in searching for materials with desirable performance.