Computation-guided exploration of the reaction parameter space of N,N-dimethylformamide hydrolysis

Abstract

Navigating the reaction parameter space can pose challenges, especially considering the exponential growth in the number of parameters even in seemingly straightforward chemical reactions or formulations. Consequently, recent research efforts have been increasingly dedicated to the development of computational tools aimed at facilitating the exploration process. Herein, we introduce ChemSPX, a Python-based program specifically crafted for exploring the complex landscape of reaction parameter space. We propose the use of the inverse distance function to map reaction parameter space and efficiently sample sparse regions. This is implemented in ChemSPX to allow the user to simply generate sets of reaction conditions that efficiently sample wide parameter spaces. In addition, the program includes tools necessary for the analysis and comprehension of the multidimensional parameter space landscape. The developed algorithms were utilized to experimentally investigate the hydrolysis of N,N-dimethylformamide (DMF), a commonly employed solvent, in the specific context of metal–organic framework synthesis. We use ChemSPX to generate batches of experiments to sample parameter space, starting from an empty space, but subsequently assessing under-sampled regions. We use statistical analysis and machine learning models to show that addition of strong acids induces hydrolysis, generating up to 1.0% (w/w) formic acid. The results show that ChemSPX can generate datasets that efficiently sample parameter space, in this case allowing the user to distinguish the individual effects of five different physical and chemical variables on reaction outcome.