In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

This paper discusses an approach to convert the longitudinal dynamics of the Cessna Citation into a variable stability platform using response feedback, thereby enabling it to take different positions on the Control Anticipation Parameter (CAP) handling quality criterion. The different positions of an aircraft on the criterion reflect different handling qualities, short period damping ratios and CAP. An experiment is performed to investigate whether the handling qualities found analytically, as a result of a variable stability control law, match those found practically from pilot tests on an aircraft simulator. Results of the experiment show that pilots are able to sensitive to even small changes in aircraft dynamics caused by the variable stability system, however, their judgment of the handling qualities does not always agree with handling qualities predicted analytically from the CAP criterion.