Functional materials can be characterized as materials that have tunable properties and are attractive solutions to the improvement and optimization of processes that require specific physiochemical characteristics. Through tailoring and altering these materials, their characteristics can be fine-tuned for specific applications. Computational modeling proves to be a crucial methodology in the design and optimization of such materials. This dissertation encompasses the utilization of molecular dynamics simulations and quantum calculations in two fields of functional materials: electrolytes and semiconductors. Molecular dynamics (MD) simulations were performed on ionic liquid-based electrolyte systems to identify molecular interactions, structural changes, and transport properties that are often reflected in experimental results. The simulations aid in the development process of the electrolyte systems in terms of concentrations of the constituents and can be invoked as a complementary or predictive tool to laboratory experiments. The theme of this study stretches further to include computational studies of the reactivity of atomic layer deposition (ALD) precursors. Selected aminosilane-based precursors were chosen to undergo density functional theory (DFT) calculations to determine surface reactivity and viability in an industrial setting. The calculations were expanded to include the testing of a semi-empirical tight binding program to predict growth per cycle and precursor reactivity with a high surface coverage model. Overall, the implementation of computational methodologies and techniques within these applications improves materials design and process efficiency while streamlining the development of new functional materials.

Current Li-ion battery technologies are limited by the low capacities of theelectrode materials and require developments to meet stringent performance demands for future energy storage devices. Electrode materials that alloy with Li, such as Si, are one of the most promising alternatives for Li-ion battery anodes due to their high capacities. Tetrel (Si, Ge, Sn) clathrates are a class of host-guest crystalline structures in which Tetrel elements form a cage framework and encapsulate metal guest atoms. These structures can form with defects such as framework/guest atom substitutions and vacancies which result in a wide design space for tuning materials properties. The goal of this work is to establish structure property relationships within the context of Li-ion battery anode applications. The type I Ba 8 Al y Ge 46-y clathrates are investigated for their electrochemical reactions with Li and show high capacities indicative of alloying reactions. DFT calculations show that Li insertion into the framework vacancies is favorable, but the migration barriers are too high for room temperature diffusion. Then, guest free type I clathrates are investigated for their Li and Na migration barriers. The results show that Li migration in the clathrate frameworks have low energy barriers (0.1- 0.4 eV) which suggest the possibility for room temperature diffusion. Then, the guest free, type II Si clathrate (Na 1 Si 136 ) is synthesized and reversible Li insertion into the type II Si clathrate structure is demonstrated. Based on the reasonable capacity (230 mAh/g), low reaction voltage (0.30 V) and low volume expansion (0.21 %), the Si clathrate could be a promising insertion anode for Li-ion batteries. Next, synchrotron X-ray measurements and pair distribution function (PDF) analysis are used to investigate the lithiation pathways of Ba 8 Ge 43 , Ba 8 Al 16 Ge 30 , Ba 8 Ga 15 Sn 31 and Na 0.3 Si 136 . The results show that the Ba-clathrates undergo amorphous phase transformations which is distinct from their elemental analogues (Ge, Sn) which feature crystalline lithiation pathways. Based on the high capacities and solid-solution reaction mechanism, guest-filled clathrates could be promising precursors to form alloying anodes with novel electrochemical properties. Finally, several high temperature (300-550 °C) electrochemical synthesis methods for Na-Si and Na-Ge clathrates are demonstrated in a cell using a Na β’’-alumina solid electrolyte.

A new class of electronic materials from food and foodstuff was developed to form a “toolkit” for edible electronics along with inorganic materials. Electrical components like resistors, capacitors and inductors were fabricated with such materials and tested. Applicable devices such as filters, microphones and pH sensors were built with edible materials. Among the applications, a wireless edible pH sensor was optimized in terms of form factor, fabrication process and cost. This dissertation discusses the material sciences of food industry, design and fabrication of electronics and biomedical engineering by demonstrating edible electronic materials, components and devices such as filters, microphones and pH sensors. pH sensors are optimized for two different generations of design and fabrication.

2D materials with reduced symmetry have gained great interest in the past decade due to the arising quantum properties introduced by the structural asymmetry. A particular example is called 2D Janus materials. Named after Roman god Janus with two faces, Janus materials have different chemical compositions on the two sides of materials, leading to a structure with broken mirror symmetry. Electronegativity difference of the facial elements induces a built-in polarization field pointing out of the plane, which has driven a lot of theory predictions on Rashba splitting, high- temperature ferromagnetism, Skyrmion formation, and so on. Previously reported experimental synthesis of Janus 2D materials relies on high-temperature processing, which limits the crystallinity of as produced 2D layers. In this dissertation, I present a room temperature selective epitaxial atomic re- placement (SEAR) method to convert CVD-grown transition metal dichalcogenides (TMDs) into a Janus structure. Chemically reactive H2 plasma is used to selectively etch off the top layer of chalcogen atoms and the introduction of replacement chalco- gen source in-situ allows for the achievement of Janus structures in one step at room temperature. It is confirmed that the produced Janus monolayers possess high crys- tallinity and good excitonic properties. Moving forward, I show the fabrication of lateral and vertical heterostructures of Janus materials, which are predicted to show exotic properties because of the intrinsic polarization field. To efficiently screen other kinds of interesting Janus structures, a new plasma chamber is designed to allow in-situ optical measurement on the target monolayer during the SEAR process. Successful conversion is seen on mechanically exfoliated MoSe2 and WSe2, and insights into reaction kinetics are gain from Raman spectra evolution. Using the monitoring ability, Janus SNbSe is synthesized for the first time. It’s also demonstrated that the overall crystallinity of as produced Janus monolayer SWSe and SMoSe are correlated with the source of monolayer TMDs. Overall, the synthesis of the Janus monolayers using the described method paves the way to the production of highly crystalline Janus materials, and with the in-situ monitoring ability, a deeper understanding of the mechanism is reached. This will accelerate future exploration of other Janus materials synthesis, and confirmation and discovery of their exciting quantum properties.

Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity of any particular alloy require very heavy computationeven for a relatively small number of atoms. This thesis aims to run conventionalmolecular dynamic simulations for a particular supercell and then employ a machinelearning based approach and compare the two in hopes of developing a method togreatly reduce computational costs as well as increase the scale and time frame ofthese systems. Conventional simulations were run using interatomic potentials basedon density function theory-basedab initiocalculations. Then deep learning neuralnetwork based interatomic potentials were used run similar simulations to comparethe two approaches.