Volume 6 Issue 2, February 2023

Data science and machine learning have the potential to accelerate the discovery of effective catalysts; however, these approaches are currently held back by the issue of negative results. This Comment highlights the value of negative data by assessing the bottlenecks in data-driven catalysis research and presents a vision for a way forwards.

Reaction networks provide complete mechanistic understanding of catalytic processes, although they can be highly complex and thus very challenging to obtain. This Perspective discusses the use of machine learning for the exploration of reaction networks in heterogeneous catalysis.

Computational chemistry has the potential to aid in the design of heterogeneous catalysts; however, there is currently a large gap between the complexity of real systems and what can be readily computed at scale. This Review discusses the ways in which machine learning can assist in closing this gap to facilitate rapid advances in catalyst discovery.

Retrobiosynthesis aims to create novel biosynthetic pathways for the beneficial production of molecules of interest. This Review outlines how machine learning can help to advance retrobiosynthesis by improving retrosynthesis planning, enzyme identification and selection, and the engineering of enzymes and pathways.

The design of complementary catalysts to target different C–H bonds in a specific molecule is challenging. Now, a pair of P450-based carbene transferase enzymes is engineered, which can selectively cyanomethylate either a C(sp³)–H or arene C(sp²)–H bond present in the same substrate.

The cleavage of C–C bonds in hydrocarbons is traditionally entrusted to precious metal catalysts, whereas common non-reducible oxides are considered unreactive. Now, the authors report nanostructured silica-embedded zirconia nanoparticles that are competent for the hydrogenolysis of polyethylene with remarkable performance.

Lithium–sulfur batteries are promising energy storage devices where catalysis can play an important role, but developing design principles for optimal performance remains a challenge. Now, a series of p-block metal sulfide cathodes are evaluated, revealing a direct correlation between the p electron gain of sulfur in the sulfide material and the apparent activation energy for the sulfur reduction reaction.

The practical optimizations of heterogeneous catalytic processes and reactor engineering are intertwined, but often what occurs inside the reactor remains elusive. Now, the molecular diffusion and carbon number of hydrocarbon products during Fischer–Tropsch synthesis on a Ru/TiO₂ catalyst are spatially resolved via magnetic resonance imaging in a pilot-scale fixed-bed reactor.

Hydrofunctionalization of α-olefins with mineral acids usually proceeds with Markovnikov selectivity. Now, a strategy based on synergistic phase transfer and photoredox catalysis is developed to facilitate anti-Markovnikov addition of aqueous hydrochloric and nitric acid to unactivated alkenes.

Reforming of methane with H₂S bears a potential for the practical generation of hydrogen from sour natural gas but remains underutilized. Here the authors analyse the reactivity of metal oxides of group 4–6 elements, which are commonly regarded as inert supports for methane activation, and highlight the substantial reactivity of these material ascribed to highly dynamic cation-bound sulfur species.