Abstract
The discovery of new catalytically active materials is one of the holy grails of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources and reduce the energy consumption of chemical industry. Indeed, heterogeneous catalysis is essential for the production of synthetic fuels and many commodity chemicals. Consequently, novel solid catalysts with higher activity and selectivity, increased sustainability and longevity, or improved prospects for rejuvenation and cyclability are needed for a diverse range of processes. Unfortunately, computational catalyst discovery is a daunting task, among other reasons because it is often unclear whether a proposed material is stable or synthesizable. This perspective proposes a new approach to this challenge, namely the use of generative grammars. We outline how grammars can guide the search for stable catalysts in a large chemical space and sketch out several research directions that would make this technology applicable to real materials.
Highlights
Heterogeneous catalysis is an essential technology for enabling sustainable economic development.[1,2] On one hand, chemical processes like ammonia synthesis require massive amounts of energy and are substantial greenhouse gas emitters
Each production rule is a prescription of how the nonterminal symbols of a language can be replaced or modified
One rule specifies that the nonterminal symbol Adj can be replaced by one of the terminal symbols heterogeneous, young, and promising
Summary
Heterogeneous catalysis is an essential technology for enabling sustainable economic development.[1,2] On one hand, chemical processes like ammonia synthesis require massive amounts of energy and are substantial greenhouse gas emitters. Such a computational catalyst screening requires first to define a library of candidates (i.e., a chemical space, see Figure 1) This space is typically constructed according to some simple rules (e.g., the set of ordered metals or solid solution alloys in a fixed lattice) or taken from some predefined experimental or computational database (e.g., the Materials Project[6]). Once this space is defined, the screening itself consists of computationally estimating the catalytic activity of all candidates (or representative samples) contained therein. It is the purpose of this perspective to argue that a good way to Received: December 3, 2021 Revised: January 13, 2022
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