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Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy that enabled delocalized and asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration of molecular gain materials for organic solid-state lasers as a frontier application in molecular optoelectronics. Distributed robotic synthesis and in-line property characterization, orchestrated by a cloud-based artificial intelligence experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Gram-scale synthesis ultimately allowed for the verification of best-in-class stimulated emission in a thin-film device. Demonstrating the asynchronous integration of five laboratories across the globe, this workflow provides a blueprint for delocalizing-and democratizing-scientific discovery.

Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of "hybrid" algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.

Excited (triplet) states offer a myriad of attractive synthetic pathways, including cycloadditions, selective homolytic bond cleavages and strain-release chemistry, isomerizations, deracemizations, or the fusion with metal catalysis. Recent years have seen enormous advantages in enabling these reactivity modes through visible-light-mediated triplet-triplet energy transfer catalysis (TTEnT). This tutorial review provides an overview of this emerging strategy for synthesizing sought-after organic motifs in a mild, selective, and sustainable manner. Building on the photophysical foundations of energy transfer, this review also discusses catalyst design, as well as the challenges and opportunities of energy transfer catalysis.

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science., (© 2022 The Author(s).)

Assessing the outcomes of chemical reactions in a quantitative fashion has been a cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data-driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities of high-quality data, the availability of which is limited: Main reasons for this include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In a series of case studies, we investigate the impact of these biases for drawing general conclusions from chemical reaction data, revealing the utmost importance of "negative" examples. Eventually, case studies into data expansion approaches showcase directions to circumvent these limitations-and demonstrate perspectives towards a long-term data quality enhancement in chemistry., (© 2022 Wiley-VCH GmbH.)

Herein, we present a photocatalyzed two-carbon ring expansion of β-dicarbonyl compounds with unactivated olefins that provides facile access to medium-sized rings. Selective sensitization of the substoichiometric enol tautomer enables reactivity of substrates incompatible with the classical De Mayo reaction conditions. Key to success is the identification of the metal-based sensitizer fac-[Ir(CF 3 -pmb) 3 ], which can be excited using common near-visible LEDs, and possesses a high triplet excited state energy of 73.3 kcal mol -1 . This exactly falls in the range between the triplet energies of the enol and keto tautomer, thereby enabling a dynamic kinetic sensitization. Demonstrating the applicability of fac-[Ir(CF 3 -pmb) 3 ] as a photocatalyst in organic synthesis for the first time, we describe a two-step photocycloaddition-ring-opening cascade with β-ketoesters, -diketones, and -ketoamides. The mechanism has been corroborated by time-resolved spectroscopy, as well as further experimental and computational studies., (© 2021 Wiley-VCH GmbH.)

The development of organic materials with high solid-state luminescence efficiency is highly desirable because of their fundamental importance and applicability in optoelectronics. Herein, a rapid construction of novel BF 2 complexes with N,O-bidentate ligands by using Cu(BF 4 ) 2 •6H 2 O as a catalyst and BF 2 source is disclosed, which avoids the need for pre-composing the N,O-bidentate ligands and features a broad substrate scope and a high tolerance level for sensitive functional groups. Moreover, molecular oxygen is employed as the terminal oxidant in this transformation. A library of 36 compounds as a new class of BF 2 complexes with remarkable photophysical properties is delivered in good to excellent yields, showing a substituent-dependency on the photophysical properties, derived from the π-π* character of the photoexcited state. In addition, aggregation-induced emission (AIE) is observed and quantified for the brightest exemplars. The excited state properties are fully investigated in solids and in THF/H 2 O mixtures. Hence, a new series of photofunctional materials with variable photophysical properties is reported, with potential applications for sensing, bioimaging, and optoelectronics., (© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Organic BF 2 complexes exhibit characteristics such as large Stokes shift, high quantum yield, strong emission intensity, and robust chemical stability, thereby being extensively used in various applications. Herein, we disclose a novel copper-catalyzed cascade C-H activation/acyloxylation and difluoroboronation of 2-phenylpyridine derivatives, thus providing a straightforward and rapid gateway to a series of N,O-bidentate organic BF 2 complexes with excellent photophysical properties. Mechanism studies demonstrate that AgBF 4 services as BF 2 source and oxidant for this elegant transformation. Most of these BF 2 complexes have broad and intense absorption and emission bands, and display bright and intensive blue fluorescence as well as large Stokes shifts., (© 2020 Wiley-VCH GmbH.)

Machine learning (ML) has emerged as a general, problem-solving paradigm with many applications in computer vision, natural language processing, digital safety, or medicine. By recognizing complex patterns in data, ML bears the potential to modernise the way how many chemical challenges are approached. In this review, an introduction to ML is given from the perspective of synthetic chemistry: starting from the fundamentals regarding algorithms and best-practice workflows, the review covers different applications of machine learning in synthesis planning, property prediction, molecular design, and reactivity prediction. In particular, different approaches of representing and utilizing organic molecules will be discussed - providing synthetic chemists both with the understanding and the tools required to apply machine learning in the context of their research, and pointers for further studying.

Gaining an understanding of the conformational behavior of fluorinated compounds would allow for expansion of the current molecular design toolbox. In order to facilitate drug discovery efforts, a systematic survey of a series of diversely substituted and protected fluorinated piperidine derivatives has been carried out using NMR spectroscopy. Computational investigations reveal that, in addition to established delocalization forces such as charge-dipole interactions and hyperconjugation, solvation and solvent polarity play a major role. This work codifies a new design principle for conformationally rigid molecular scaffolds., (© 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

An intermolecular, two-component vicinal carboimination of alkenes has been accomplished by energy transfer catalysis. Oxime esters of alkyl carboxylic acids were used as bifunctional reagents to generate both alkyl and iminyl radicals. Subsequently, addition of the alkyl radical to an alkene generates a transient radical for selective radical-radical cross-coupling with the persistent iminyl radical. Furthermore, this process provides direct access to aliphatic primary amines and α-amino acids by simple hydrolysis., (© 2019 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.)

Investigations into the selectivity of intermolecular alkyl radical additions to C-O- vs. C-C-double bonds in α,β-unsaturated carbonyl compounds are described. Therefore, a photoredox-initiated radical chain reaction is explored, where the activation of the carbonyl-group through an in situ generated Lewis acid - originating from the substrate - enables the formation of either C-O or the C-C-addition products. α,β-Unsaturated aldehydes form selectively 1,2-, while esters and ketones form the corresponding 1,4-addition products exclusively. Computational studies lead to reason that this chemo- and regioselectivity is determined by the consecutive step, i.e. an electron transfer, after reversible radical addition, which eventually propagates the radical chain., (This journal is © The Royal Society of Chemistry 2020.)

Despite significant progress in aliphatic decarboxylation, an efficient and general protocol for radical aromatic decarboxylation has lagged far behind. Herein, we describe a general strategy for rapid access to both aryl and alkyl radicals by photosensitized decarboxylation of the corresponding carboxylic acids esters followed by their successive use in divergent carbon-heteroatom and carbon-carbon bond-forming reactions. Identification of a suitable activator for carboxylic acids is the key to bypass a competing single-electron-transfer mechanism and "switch on" an energy-transfer-mediated homolysis of unsymmetrical σ-bonds for a concerted fragmentation/decarboxylation process., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The development and application of traceless acceptor groups in photochemical C-C bond formation is described. This strategy was enabled by the photoexcitation of electron donor-acceptor (EDA) complexes with visible light. The traceless acceptors, which were readily prepared from amino acid and peptide feedstocks, could be used to alkylate a wide range of heteroarene and enamine donors under metal- and peroxide-free conditions. The crucial role of the EDA complexes in the mechanism of these reactions was explored through combined experimental and computational studies., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A visible-light-mediated approach to carbonyl-olefin cross-metathesis is described. Photoinduced hole catalysis was used to promote the formation of 1,3-diols from aldehydes and styrenes, which were then readily fragmented under acidic conditions to form the cross-metathesis products. The use of 1,3-diols as intermediates, rather than the energetically more demanding oxetanes, provides a new, orthogonal mechanistic strategy for carbonyl-olefin cross-metathesis. Furthermore, this approach does not require any metals, ligands, or additives, and provides the products with high levels of E selectivity. A mechanistic rationale is provided and supported by both theoretical calculations and experiments. Additionally, a practical synthesis of a new acridinium-based photocatalyst, including full characterization, is presented., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A deaminative strategy for the borylation of aliphatic primary amines is described. Alkyl radicals derived from the single-electron reduction of redox-active pyridinium salts, which can be isolated or generated in situ, were borylated in a visible light-mediated reaction with bis(catecholato)diboron. No catalyst or further additives were required. The key electron donor-acceptor complex was characterized in detail by both experimental and computational investigations. The synthetic potential of this mild protocol was demonstrated through the late-stage functionalization of natural products and drug molecules., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Harnessing visible light to access excited (triplet) states of organic compounds can enable impressive reactivity modes. This tutorial review covers the photophysical fundamentals and most significant advances in the field of visible-light-mediated energy transfer catalysis within the last decade. Methods to determine excited triplet state energies and to characterize the underlying Dexter energy transfer are discussed. Synthetic applications of this field, divided into four main categories (cyclization reactions, double bond isomerizations, bond dissociations and sensitization of metal complexes), are also examined.

Sulfur-containing molecules participate in many essential biological processes. Of utmost importance is the methylthioether moiety, present in the proteinogenic amino acid methionine and installed in tRNA by radical-S-adenosylmethionine methylthiotransferases. Although the thiol-ene reaction for carbon-sulfur bond formation has found widespread applications in materials or medicinal science, a biocompatible chemo- and regioselective hydrothiolation of unactivated alkenes and alkynes remains elusive. Here, we describe the design of a general chemoselective anti-Markovnikov hydroalkyl/aryl thiolation of alkenes and alkynes-also allowing the biologically important hydromethylthiolation-by triplet-triplet energy transfer activation of disulfides. This fast disulfide-ene reaction shows extraordinary functional group tolerance and biocompatibility. Transient absorption spectroscopy was used to study the sensitization process in detail. The hereby gained mechanistic insights were successfully employed for optimization of the catalytic system. This photosensitized transformation should stimulate bioimaging applications and carbon-sulfur bond-forming late-stage functionalization chemistry, especially in the context of metabolic labelling.

The discovery and application of dearomative cascade photocatalysis as a strategy in complex molecule synthesis is described. Visible-light-absorbing photosensitizers were used to (sequentially) activate a 1-naphthol derived arene precursor to divergently form two different polycyclic molecular scaffolds through catalyst selective energy transfer.

Herein, we report a novel strategy for the activation of DMSO to act as a versatile alkylating agent in heteroarene C-H functionalization. This direct, simple, and mild switch between methylation/trideuteromethylation and methylthiomethylation of heteroarenes was achieved under reagent-controlled photoredox catalysis conditions. The proposed mechanism is supported by both experimental and computational studies., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Herein, we introduce a new class of bench-stable N -heterocyclic carbene (NHC) nickel-precatalysts for homogeneous nickel-catalysis. The nickel(II) complexes are readily activated to Ni 0 in situ under mild conditions, via a proposed Heck-type mechanism. The precatalysts are shown to facilitate carbonyl-ene, hydroalkenylation, and amination reactions., Competing Interests: The authors declare no conflict of interest.

Herein, we present a novel strategy for the utilization of simple carbonyl compounds, aldehydes and ketones, as intermolecular radical acceptors. The reaction is enabled by visible light photoredox initiated hole catalysis and the in situ Brønsted acid activation of the carbonyl compound. This regioselective alkyl radical addition reaction does not require metals, ligands or additives and proceeds with a high degree of atom economy under mild conditions. The proposed mechanism is supported by both experimental and theoretical studies.