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Page 10 of 33 Liu et al. J Mater Inf 2024;4:33 https://dx.doi.org/10.20517/jmi.2024.48

photocatalysts, including increasing the band gap and enhancing charge separation. These calculated
modifications are essential for reducing electron-hole recombination and ultimately improving
[77]
photocatalytic efficiency, addressing complex design challenges effectively .

As listed in Table 3, several widely used software packages are central to these calculations. VASP (Vienna
Ab initio Simulation Package), one of the most commonly used tools, integrates projector augmented wave
(PAW)/Perdew-Burke-Ernzerh (PBE) modules and hybrid functionals such as HSE06 for more accurate
electronic structure calculations. Quantum ESPRESSO, known for its use of the PBE functional and DFT-
D2 for dispersion corrections, is frequently employed for band gap and optical absorption predictions.
Additionally, Materials Studio, with modules such as CASTEP, offers robust capabilities for calculating
electronic properties, including band structure and light absorption coefficients. These software tools,
combined with the growing computational power, enable researchers to predict the performance of
potential photocatalysts with high precision, guiding experimental validation and accelerating the discovery
process. These computational parameters, such as band gap, adsorption energy, and Gibbs free energy, are
not only applicable to specific photocatalysts but also have broad applicability across various
semiconductors for predicting photocatalytic activity. The universality of these first-principles-derived
parameters across different systems provides a solid foundation for the theoretical design of photocatalysts.
Through accurate calculations, these parameters help identify candidates with optimal electronic structures,
thereby accelerating the development of efficient photocatalysts.

Table 3 also lists some classical works of photocatalyst designs based on first-principles calculation. One
notable example exhibited in Figure 4A is the work by Shen et al., where DFT calculations played a pivotal
role in designing a defective ZnTi-LDHs/Ti C O photocatalyst aimed at improving the selectivity for C
3 2
2
2
organics in the photocatalytic reduction of CO . The DFT simulations revealed that the formation of a
[78]
2
Schottky junction significantly enhanced charge separation and increased the concentration of
photogenerated electrons on the photocatalyst surface, while oxygen vacancies facilitated CO adsorption
2
and activation by lowering the Gibbs free energy barrier for CO intermediate formation. This DFT-guided
strategy not only optimized C selectivity but also provided detailed insights into the reaction mechanisms,
2
underscoring the crucial role of first-principles calculations in advancing the design of photocatalysts for
high-value product formation. In another example shown in Figure 4B, Wang et al. utilized DFT to guide
the molecular engineering of g-C N through the integration of donor-acceptor units to address inherent
4
3
structural defects . DFT calculations were employed to predict the optimal electron-donating unit, with
[79]
benzaldehyde identified as the ideal candidate to enhance intramolecular charge transfer and narrow the
band gap. This theoretical prediction was validated experimentally, where the modified g-C N
3
4
demonstrated a 3.73-fold improvement in photocatalytic tetracycline degradation compared to the
unmodified version, indicating the critical role of DFT in optimizing photocatalysts for environmental
applications. In summary, first-principles calculations, particularly those grounded in DFT, have proven
indispensable in predicting and fine-tuning the electronic and structural properties of photocatalysts,
thereby facilitating the rational design of highly efficient systems across a wide range of applications.

HTS
HTS [91,92] is a technique that rapidly evaluates large sets of materials through the integration of
computational and experimental methods, facilitating the efficient identification of candidates with optimal
properties. It is instrumental in materials science, allowing for the automation of the evaluation process,
which significantly accelerates the discovery and optimization of novel materials. HTS allows for the swift
screening of various compositions, surface modifications, and structural configurations, identifying those
with enhanced photocatalytic performance. This method reduces the time and resources typically required
for experimental validation and expands the search space for high-performance photocatalysts. The HTS