Steiner Miguel, Reiher Markus
ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
ETH Zurich, NCCR Catalysis, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.
Nat Commun. 2024 May 1;15(1):3680. doi: 10.1038/s41467-024-47997-9.
Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.
自主反应网络探索算法提供了一种系统的方法来探索复杂化学过程的机制。然而，由此产生的反应网络非常庞大，以至于对所有潜在可及中间体进行探索在计算上要求过高。这使得蛮力探索不可行，而使用完全预定义的中间体或硬编码化学约束（如特定元素的配位数）进行探索对于复杂化学系统来说灵活性不足。在此，我们引入一种“方向盘”来指导原本无偏的自动探索。“方向盘”算法直观、普遍适用，能使人们专注于新兴网络的特定区域。它还允许在机理探索、催化剂设计及其他化学优化挑战的背景下指导自动数据生成。该算法在过渡金属催化剂反应机理阐明中得到了验证。我们强调了如何以系统且可重复的方式探索催化循环。探索目标完全可调，使人们能够利用“方向盘”进行特定结构（精确）计算以及对可能的反应中间体进行广泛的高通量筛选。
