A human-machine interface for automatic exploration of chemical reaction networks

Abstract

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.

Reaction Networks Resemble Low-Dimensional Regular Lattices

“…Exploration of PESs is currently a very active area of research. In addition to rules-based methods by us 13,14,38 and others, 9,10,39-42 accelerated molecular dynamics methods are important tools for exploring PES, for example, metadynamics, [43][44][45] artificial force-based, [46][47][48] manual steering, 49 and ab initio nanoreactor [50][51][52][53] approaches. Moreover, reaction templatebased methods [54][55][56][57] and machine learning approaches [58][59][60][61] have been applied with success.…”

Overcoming the Pitfalls of Computing Reaction Selectivity from Ensembles of Transition States

“…Relying on computational methods, such as density functional theory (DFT), to accurately predict reaction selectivity remains a key challenge for in silico catalyst design. − Small errors in computed transition state (TS) energies, even those below chemical accuracy (1 kcal/mol), can result in a reversal of predicted selectivity due to the exponential relationship between effective activation energies and rate constants. − Dealing with these accuracy issues can further be complicated when large and flexible functional groups used to impart asymmetry through noncovalent interactions are present, − as these larger systems are likely to adopt multiple TS conformations.…”

“…Improper treatment resulting in “double counting” in this setting would lead to an artificial decrease in the effective activation energy. Note that differentiating between conformational isomers and rotamers is a recurrent challenge in automated reaction network exploration. ,,− …”

“…Note that differentiating between conformational isomers and rotamers is a recurrent challenge in automated reaction network exploration. 11 , 12 , 68 − 71 …”