Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Abstract

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system. Graphical Abstract

Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective

“…However, the recent and rapidly increasing interest in data driven catalysis research has a strong emphasis on computational chemistry. 22,38,40,41,53,57,[105][106][107][108][109][110][111] Examples are the bottom-up prediction of crystal structures, 112 new mixed oxides, 113 or alloys and intermetallic compounds 114 from first principles, establishing scaling relationships between the adsorption energies of reactants and activation reaction energies (Brønsted-Evans-Polanyi (BEP) relationships), 115,116 the fast exploration of potential energy surfaces, 117 and addressing complex reaction networks. 57 In addition, literature data have also been used for data mining and knowledge extraction, 41,[118][119][120][121][122][123] although this approach is problematic as often only "good" catalyst data are published, 124 and the dataset thus consists of many similar results, which are sometimes incomplete and poorly documented.…”

Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis

“…Combined with active learning − and global optimization algorithms, − catalysts with optimal catalytic properties can be quickly identified for a given mechanism. Because it is often difficult to experimentally characterize all mechanistically relevant intermediates due to their transient nature, computational approaches that explore reaction mechanisms are also desired. , In this case, ML combined with automated VHTS workflows can accelerate the exploration of potential reactive intermediates and reaction pathways. − …”

The (not so) simple prediction of enantioselectivity – a pipeline for high-fidelity computations

“…Computational modeling of catalytic processes, oen achieved through the creation of free energy proles based on density functional theory computations, can provide key information about viable mechanistic pathways. [1][2][3][4][5] First-principles calculations are not only used to rationalize experimental results, but also to optimize the activity/selectivity of a desired chemical transformation through catalyst design. [6][7][8][9][10] In this regard, experimental strategies involve the judicious placement of functional groups possessing tuned stereoelectronic and steric elements with the aim of inducing high enantioselectivity.…”