Hosseini Hajar, Herring Connor J, Nwaokorie Chukwudi F, Sulley Gloria A, Montemore Matthew M
Department of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118, United States.
J Phys Chem C Nanomater Interfaces. 2024 Oct 17;128(43):18144-18157. doi: 10.1021/acs.jpcc.4c04949. eCollection 2024 Oct 31.
Computation has long proven useful in understanding heterogeneous catalysts and rationalizing experimental findings. However, computational design with experimental validation requires somewhat different approaches and has proven more difficult. In recent years, there have been increasing successes in such computational design with experimental validation. In this Perspective, we discuss some of these recent successes and the methodologies used. We also discuss various design strategies more broadly, as well as approximations to consider and pitfalls to try to avoid when designing for experiment. Overall, computation can be a powerful and efficient tool in guiding catalyst design but must be combined with a strong fundamental understanding of catalysis science to be most effective in terms of both choosing the design methodology and choosing which materials to pursue experimentally.
长期以来，计算在理解多相催化剂和合理化实验结果方面已证明是有用的。然而，通过实验验证进行计算设计需要略有不同的方法，并且已证明更具挑战性。近年来，这种通过实验验证的计算设计取得了越来越多的成功。在这篇透视文章中，我们讨论了其中一些近期的成功案例以及所使用的方法。我们还更广泛地讨论了各种设计策略，以及在为实验进行设计时要考虑的近似方法和要避免的陷阱。总体而言，计算可以成为指导催化剂设计的强大而有效的工具，但必须与对催化科学的深入基础理解相结合，才能在选择设计方法和选择哪些材料进行实验方面最有效。
