Hariharan Seenivasan, Kinge Sachin, Visscher Lucas
Institute for Theoretical Physics, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands.
QuSoft, Science Park 123, 1098 XG Amsterdam, The Netherlands.
J Chem Inf Model. 2025 Jan 27;65(2):472-511. doi: 10.1021/acs.jcim.4c01212. Epub 2024 Nov 29.
Heterogeneous catalysis plays a critical role in many industrial processes, including the production of fuels, chemicals, and pharmaceuticals, and research to improve current catalytic processes is important to make the chemical industry more sustainable. Despite its importance, the challenge of identifying optimal catalysts with the required activity and selectivity persists, demanding a detailed understanding of the complex interactions between catalysts and reactants at various length and time scales. Density functional theory (DFT) has been the workhorse in modeling heterogeneous catalysis for more than three decades. While DFT has been instrumental, this review explores the application of quantum computing algorithms in modeling heterogeneous catalysis, which could bring a paradigm shift in our approach to understanding catalytic interfaces. Bridging academic and industrial perspectives by focusing on emerging materials, such as multicomponent alloys, single-atom catalysts, and magnetic catalysts, we delve into the limitations of DFT in capturing strong correlation effects and spin-related phenomena. The review also presents important algorithms and their applications relevant to heterogeneous catalysis modeling to showcase advancements in the field. Additionally, the review explores embedding strategies where quantum computing algorithms handle strongly correlated regions, while traditional quantum chemistry algorithms address the remainder, thereby offering a promising approach for large-scale heterogeneous catalysis modeling. Looking forward, ongoing investments by academia and industry reflect a growing enthusiasm for quantum computing's potential in heterogeneous catalysis research. The review concludes by envisioning a future where quantum computing algorithms seamlessly integrate into research workflows, propelling us into a new era of computational chemistry and thereby reshaping the landscape of modeling heterogeneous catalysis.
多相催化在许多工业过程中起着关键作用，包括燃料、化学品和药品的生产，而改进当前催化过程的研究对于使化学工业更具可持续性至关重要。尽管其很重要，但识别具有所需活性和选择性的最佳催化剂这一挑战依然存在，这需要详细了解催化剂与反应物在各种长度和时间尺度上的复杂相互作用。三十多年来，密度泛函理论（DFT）一直是多相催化建模的主力军。虽然DFT发挥了重要作用，但本综述探讨了量子计算算法在多相催化建模中的应用，这可能会给我们理解催化界面的方法带来范式转变。通过关注多组分合金、单原子催化剂和磁性催化剂等新兴材料，弥合学术和工业视角，我们深入研究了DFT在捕捉强关联效应和自旋相关现象方面的局限性。本综述还介绍了与多相催化建模相关的重要算法及其应用，以展示该领域的进展。此外，本综述探讨了嵌入策略，即量子计算算法处理强关联区域，而传统量子化学算法处理其余部分，从而为大规模多相催化建模提供了一种有前景的方法。展望未来，学术界和工业界的持续投资反映出对量子计算在多相催化研究中的潜力的热情日益高涨。本综述最后设想了一个未来，量子计算算法无缝集成到研究工作流程中，推动我们进入计算化学的新时代，从而重塑多相催化建模的格局。
