Electrochemical CO2 reduction reaction (ECO2RR) powered by renewable electricity possesses the potential to store intermittent energy in chemical bonds while producing sustainable chemicals and fuels. Unfortunately, it is hard to achieve low overpotential, high selectivity, and activity simultaneously of ECO2RR. Developing efficient electrocatalysts is the most promising strategy to enhance electrocatalytic activity in CO2 reduction. Herein, we designed novel Bi-Cu2S heterostructures by a one-pot wet-chemistry method. The epitaxial growth of Cu2S on Bi results in abundant interfacial sites and these heterostructured nanocrystals demonstrated high electrocatalytic performance of ECO2RR with high current density, largely reduced overpotential, near-unity FE for formate production (Chapter 2). Meanwhile, we see a lot of opportunities for catalysis in a confined space due to their tunable microenvironment and active sites on the surface, leading to a broad spectrum of electrochemical conversion schemes. Herein, we reveal fundamental concepts of confined catalysis by summarizing recent experimental investigations. We mainly focus on carbon nanotubes (CNTs) encapsulated metal-based materials and summarize their applications in emerging electrochemical reactions, including ECO2RR and more (Chapter 3). Although we were able to obtain high activity and selectivity toward C1 products, it is more attractive to go beyond C1 chemicals to produce C2 products due to their high industrial value. Herein, we designed Ag-modified Cu alloy catalysts that can create a CO-rich local environment for enhancing C-C coupling on Cu for C2 formation. Moreover, Ag corporate in Cu can chemically improve the structural stability of Cu lattice. (Chapter 4) Nevertheless, advanced electrocatalytic platforms cannot be developed without a fundamental understanding of binding configurations of the surface-adsorbed intermediates and adsorbate-adsorbate interaction on the local environment in electrochemical CO2 reduction. In this case, we make discussions of recent developments of machine learning based models of adsorbate-adsorbate interactions, including the oversimplified linear analytic relationships, the cluster expansion models parameterized by machine learning algorithms, and the highly nonlinear deep learning models. We also discuss the challenges of the field, particularly overcoming the limitations of pure data driven models with the integration of computational theory and machine learning of lateral interactions for catalyst materials design. (Chapter 5).