With the ever-growing global energy demands and environmental pollution issues, developing high-performance energy storage and conversion materials has become a hot topic in the material science community. In this regard, substantial progress has been made in theoretically predicting new materials for energy-related fields, experimentally synthesizing these materials, and further improving their properties for high performance in energy storage and conversion devices. In particular, two-dimensional (2D) materials have shown great potential in the field of energy storage and conversion. However, it remains challenging to explore 2D materials that render high efficiency of energy storage and conversion while guarantee long-term stability and safety. Over the past decades, theoretical calculations based on density functional theory (DFT) have become a practical toolkit to address this issue by revealing the reaction mechanism at an atomic scale and screening high-performance energy storage and conversion materials on a large scale. In particular, DFT calculations enable us to establish the relationships between the intrinsic properties of materials and their performance for energy storage and conversion, and provide theoretical guidance for screening and experimentally synthesizing the promising materials. In this review, we summarize the DFT calculations’ applications in recent studies of developing high-performance and reliable energy-related 2D materials for Li-ion battery (LIB), water splitting, fuel cells, and electrochemical carbon dioxide reduction (CRR). First, we introduce the reaction mechanism of LIB, hydrogen evolution reaction (HER), oxygen evolution reaction/oxygen reduction reaction (OER/ORR), and CRR in detail and the application of 2D material in these fields. Then, we highlight the role of DFT calculations in unveiling the intrinsic relationships between the electronic structure and the performance of 2D materials by comprehensively discussing the descriptors in predicting the performance of 2D materials. For example, the occupancy of d orbital and energy required to fill empty states serve as descriptors to predict the electrochemical performance of the electrode in ion intercalation battery. The d orbital center, lowest unoccupied states, and oxygen vacancy formation energy serve as descriptors to predict the catalytic performance of electrode in HER. The energy difference between the lowest valance electron orbital center and Fermi level, occupancy of p z orbital, and the energy difference between p z and p x /p y orbital center serve as descriptors to predict the catalytic performance of electrode in ORR. Even though these descriptors can help to further understand the relationships between the electronic structure and the performance of the electrochemical electrode, they are only reliable to specific materials and inapplicable to the electrode with a complex structure or complex reaction path, such as the electrode in CRR. Newly developed machine learning methods may bring a breakthrough to the exploration of a universal descriptor, which is a key factor in the large-scale screening of potential electrode materials with excellent performance and the dependable guidance to experimental synthesis. Finally, we summarize the disadvantage of DFT calculation, such as the underestimation of bandgap and incorrect description of van der Waals interaction, and give a perspective of DFT calculations in the study of new energy-related materials. The method to simulate the ambient environment of the electrode (including the electrolyte, external electric field, and non-cooperative transfer of proton and electron) based on DFT calculation is needed to be developed, which is vital to reflect the actual working condition of the electrode. The universal descriptor applicable to the electrode with a complex structure is also needed to explore to overcome the poor versatility of single intrinsic property of the material in predicting the performance of the electrochemical electrode.
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