Abstract:
Catalysts enhance the rate of a chemical reaction by providing an alternate, low energy
barrier route connecting reactants to products, without undergoing any change themselves. At its core, the main purpose of a catalyst is to save energy. Production of clean
fuels like H2, conversion of waste and even harmful by-products (CO2) to usable moieties (CH3OH), and synthesis of industrially important chemicals (NH3) are impacted
greatly by catalysts. Determination of mechanisms of catalytic activity and its prediction
are fundamentally challenging, because it involves physical and chemical interactions between different types of molecules and solids. Prediction and design of highly selective
catalysts with efficient activity thus have huge academic and socioeconomic significance.
Substantial advancement in computational modelling, algorithms and unprecedented growth
of raw computing power have enabled the design and prediction of catalysts with calculations within first-principles density functional theory (DFT). These DFT-based simulations provide unbiased, non-empirical access to detailed atomistic and electronic structure
and properties of materials, complementing experiments. In this thesis, we demonstrate
how comprehensive analysis based on DFT calculations can be used to (a) understand and
explain the activity of experimentally synthesized catalysts and (b) design and predict
novel catalysts, for a number of reactions of relevance to tackling problems of energy and
environment. We illustrate how the activity of a catalyst can be tuned with structure, defects (vacancies), and substitutional alloying, identifying relevant descriptors that would
facilitate further work.
In Chapter 1 we give a brief introduction to some fundamental and important aspects of
catalysis. This is followed by a brief description of methods and formalism used in our
calculations in Chapter 2. Further, the thesis is divided into three parts, based on the
types of catalytic reactions studied.
