LG

Choosing a photocatalyst for a given reaction can be challenging due to complex mechanisms and multiple parameters that govern the outcome of a photocatalyzed reaction. Herein, we disclose a machine learning (ML) model that can suggest catalysts for a given reaction using an online portal. The model was experimentally validated against five photocatalysis reactions, in all cases suggesting productive photocatalysts. This model serves as a valuable tool for researchers optimizing photocatalysis reactions.

Abstract

Utilizing an extensive library of literature on photocatalytic transformations, we disclose the development of a machine learning (ML) model for the recommendation of photocatalysts most suitable for reactions of interest. The model is trained on > 36 000 such literature examples and uses an architecture inspired by the Bidirectional Encoder Representations from Transformer (BERT) large language model. Under cross-validation, it can suggest the “correct” photocatalysts with ∼90% accuracy. When experimentally tested on five out-of-box reactions, this algorithm consistently suggested photocatalysts that gave yields competitive to those chosen by human researchers and frequently suggested alternative photocatalysts that are potentially more appealing than the originally selected photocatalyst. Altogether, this platform serves as a valuable tool for researchers undertaking reaction optimization programs. The model is free to use at https://photocatals.grzybowskigroup.pl/predict/.

A machine learning model for atropisomers’ stability is developed. By integrating a physicochemically-informed axial chirality structure descriptor (ACSD) with a graph attention network (GAT), the model accurately predicts rotational barriers. Its robustness is validated on complex drugs and molecular switches, providing a powerful tool for rational catalyst and drug design.

Abstract

Atropisomers play a vital role in asymmetric synthesis, drug discovery, and the development of functional materials. However, the rational design of atropisomers is challenging due to the difficulty in predicting their configurational stability, which depends on the rotational barrier (ΔG ^‡). Here, we introduce ACSD-GAT, a deep learning framework that addresses this issue. Our approach comprises a newly curated benchmark dataset of 1015 experimentally measured rotational barrier, along with a physicochemically informed axial chirality structure descriptor (ACSD) that explicitly quantifies both static and dynamic steric repulsion during rotation. By integrating the ACSD with a graph attention network (GAT), our model accurately predicts the rotational barrier, achieving an R ² of 0.91 and a RMSE of 2.02 kcal mol⁻¹ on test datasets. The robustness and real-world applicability of the model are also demonstrated through rigorous validation with complex pharmaceuticals, molecular switches, and newly synthesized atropisomers.

Autonomous chemistry demands unbiased, adaptable tools for monitoring reactivity. We present a general method using similarity analysis of spectroscopic data, independent of prior knowledge. Demonstrated across diverse techniques and reactions, this approach reveals kinetics and dynamics, establishing a foundation for self-guided chemical platforms and accelerating the future of automated discovery.

Abstract

The development of fully autonomous chemical synthesis platforms requires robust, real-time assessment of reactivity that does not rely on prior mechanistic knowledge. Existing methods often depend on predefined reaction models or chemical intuition, limiting their generalizability and adaptability. To address this challenge, we introduce a chemically agnostic approach that quantifies reactivity dynamics by applying similarity metrics to the full informational content of in-situ spectroscopic data. Using NMR, UV/Vis, IR, and EPR spectroscopy, we demonstrate that spectral similarity trajectories can reliably indicate reaction progress, detect kinetic features such as autocatalysis, and resolve complex behaviours including oscillations. Across diverse reaction classes, this method enabled estimated end-point detection and kinetic profiling without reaction-specific tuning. For example, the formation of lophine was followed by NMR at different reaction temperatures revealing Arrhenius-type kinetics. In a different experiment, the Belousov Zhabotinsky reaction was monitored by UV/Vis and the chemical oscillation with a periodicity of 7.25 s was captured. These results establish a generalizable framework for real-time, data-driven reactivity monitoring, representing a critical step toward autonomous synthesis guided by multidimensional spectroscopic feedback.

A single propargylic alcohol beam enters a catalyst prism and splits into three product beams via adaptive rhodium catalysis assisted by a Lewis-acidic secondary sphere. In their Communication (e202515903), Christophe Werlé and co-workers describe a rhodium catalyst featuring a triazine ligand with a borane arm that selectively transforms a propargylic alcohol into retained alkynes, allylic ethers, or (E)-alkenes.

In the Research Article (e17261) by Kevin R. Wilson and co-workers, a new unified kinetic model is developed to describe the pH dependent kinetics of the Fenton reaction. By incorporating iron speciation and updated rate constants, the model reproduces experimental results and reveals that hydroxyl radicals dominate at low pH, while Fe(IV) becomes the main oxidant under near-neutral conditions.

A computational approach integrating quantum chemistry and machine learning enables the prediction of CO₂–adduct stability. Model interpretability and experimental validation highlight its utility for designing base-mediated C–H carboxylation reactions.

Abstract

Base-mediated C–H carboxylation is a versatile pathway for utilizing carbon dioxide (CO₂) as a C1 building block in organic synthesis. However, CO₂ constitutes a notorious thermodynamic sink, which restricts this approach to activated or intrinsically reactive nucleophiles. To qualitatively assess the stability of CO₂ adducts, we present a computational approach that integrates quantum chemistry with statistical modeling to build a predictive workflow. The target property is the CO₂ affinity, specifically the negative Gibbs free reaction energy. This predictive workflow has been applied to 60 novel carbon-centered nucleophiles, suggesting reactions that yield stable carboxylation adducts. The results have been validated through experimental methods for five carbanions, which include three stable and two unstable adducts in DMSO according to our predictions. In addition, we examined two further carbanions that were suggested to form stable CO₂ adducts in DMSO, to further assess the experimental protocol and broaden its scope to structurally distinct motifs.

A noncovalent strategy to activate and enhance room-temperature phosphorescence (RTP) in heavy-atom-free chromophores through external heavy-atom engineering (HAE) is reported. Incorporating these chromophores into halogen-rich polymer matrices brings them into close spatial proximity with heavy atoms, thereby facilitating the intersystem crossing process via external HAE and thus unlocking RTP.

Abstract

Room-temperature phosphorescence (RTP) from purely organic materials holds great potential for optoelectronic and bioimaging applications; however, its realization remains challenging due to inefficient intersystem crossing (ISC) and rapid nonradiative decay. Herein, we present an effective strategy to activate and enhance RTP in heavy-atom-free chromophores through external heavy-atom engineering (HAE). By dispersing these chromophores within hydrophobic halogen-rich polymer matrices, external HAE is employed to enhance spin–orbit coupling and promote efficient ISC. The generality of this strategy is demonstrated across a series of polycyclic aromatic hydrocarbons and further extended to other heterocyclic chromophores, affording bright (phosphorescence quantum yields up to 61.6%) and long-lived (phosphorescence lifetimes up to 273 ms) RTP materials under ambient conditions, with emission wavelengths spanning from 500 to 700 nm. This supramolecular approach circumvents the need for covalent introduction of heavy atoms to the luminophores, thereby preserving their intrinsic photophysical properties while unlocking desirable triplet-state emissions. Overall, the current external HAE strategy provides a versatile platform for the construction of high-performance organic RTP materials, offering new insights into the design principles of organic RTP systems and broadening the material landscape for optoelectronic and bioimaging applications.

Nature Chemistry, Published online: 11 November 2025; doi:10.1038/s41557-025-01990-x

The Pd-catalyzed aminative coupling of primary and secondary alkyl boronic esters with aryl (pseudo)halides is reported. DFT calculations and mechanistic experiments show that the first C─N bond formation may take place through an unusual dyotropic rearrangement of a LPd(Ar)NHX complex (X = OPOPh₂).

Abstract

Insertive cross-coupling reactions provide the option of repurposing widely available coupling partners for the formation of new linkages. By confronting a major limitation of the aminative Suzuki–Miyaura methods we recently reported, we achieve a general method for the Pd-catalyzed aminative coupling of primary and secondary alkyl boronic esters with aryl (pseudo)halides. Introducing a formal nitrene insertion into this Suzuki–Miyaura reaction diverts the outcome from the traditional C(sp²)─C(sp³) products to the C(sp²)─NH─C(sp³) analogues (N-aryl anilines). DFT calculations and experimental mechanistic studies indicate that C─N bond formation from the electrophilic aryl component occurs first, providing further evidence for this previously hypothetical pathway. By comparison of several transition state structures, we find that C─N bond formation likely takes place through an unusual dyotropic rearrangement of a LPd(Ar)NHX complex (X = OPOPh₂).

Nature Chemistry, Published online: 31 October 2025; doi:10.1038/s41557-025-01974-x

Nonreductive cleavage of aromatic carbon–nitrogen (C─N) bonds in pyridines is achieved via ring-opening metathesis using PCP-ligated zirconium (Zr) alkylidene complexes. Ligand control enables divergent outcomes: functionalization into conjugated iminotrienes or reductive homocoupling to bipyridine dianions, revealing new strategies for N-heterocycle activation.

Abstract

The transformation of N-heterocycles such as pyridines is of fundamental and practical importance, yet direct cleavage of their aromatic C─N bonds remains challenging due to strong electronic delocalization and intrinsic stability. Here we report an unprecedented nonreductive cleavage of the aromatic C─N bond in pyridines via direct ring-opening metathesis with PCP-ligated (PCP = 2,6-( ^t Bu₂PCH₂)₂-C₆H₃) zirconium alkylidene complexes under mild conditions. Experimental and computational studies reveal that these transformations proceed via highly reactive [Zr]═C species instead of alkylidyne pathways, forming acyclic Zr-imido-triene complexes that undergo imido/oxo exchange with acetone. This enables a concise conversion of pyridines into conjugated iminotrienes that otherwise require multistep syntheses. The ancillary ligand dictates divergent pathways: alkyl-, alkoxide-, or aryloxide-containing alkylidenes selectively promote ring-opening functionalization, whereas the chloride analogue triggers rare reductive homocoupling of pyridines to bipyridine dianions. This ligand-controlled reactivity arises from steric and electronic effects of the chloride variant, facilitating C─H activation and C─C bond formation. These findings demonstrate a novel approach to aromatic C─N bond scission and dual functionalization at both termini of the opened pyridine rings, along with a reductive homocoupling pathway to bipyridine dianions, providing new opportunities for catalytic transformations of aromatic N-heterocycles.