Machine Learning May Sometimes Simply Capture LiteraturePopularity Trends: A Case Study of Heterocyclic Suzuki-MiyauraCoupling br

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers ofliterature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paperdemonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki-Miyaura coupling with heterocyclic building blocks & xe0d5;and a carefully selected database of >10,000 literature examples & xe0d5;we show thatML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to onlysolvents and bases. This result holds irrespective of the ML model applied (from simple feed-forward to state-of-the-art graph-convolution neural networks) or the representation to describe the reaction partners (variousfingerprints, chemical descriptors,latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on thesheer frequency of certain reaction conditions reported in the literature. These unsatisfactory results likely reflect subjectivepreferences of various chemists to use certain protocols, other biasing factors as mundane as availability of certain solvents/reagents,and/or a lack of negative data. Thesefindings highlight the likely importance of systematically generating reliable and standardizeddata sets for algorithm training

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Hydrogen storage by adsorption in porous materials: is it possible?

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Hydrogen storage by adsorption in porous materials: Is it possible?

International audienceThe role of fundamental characteristics of porous systems (binding energy, specific surface area and multilayer adsorption) in designing an efficient hydrogen adsorbent is discussed. We analyze why the amount of hydrogen adsorbed in all known materials is much lower than required for mobile applications and what are possible strategies to increase it. Further we report new ab initio calculations demonstrating possible ways of chemical modification of graphene fragments which can lead to the substantial increase of hydrogen binding to the graphene-based surface. Such Open Carbon Frameworks, substituted and functionalized at the fragments' edge may theoretically adsorb, at ambient temperature and relatively low pressure (60-100 bar), the amount of hydrogen necessary for mobile applications

Unique Bonding Nature of Carbon-Substituted Be2 Dimer inside the Carbon (sp2) Network

International audienceControlled doping of active carbon materials (viz., graphenes, carbonnanotubes etc.) may lead to the enhancement of their desired properties. The leaststudied case of C/Be substitution offers an attractive possibility in this respect. Theinteractions of Be2 with Be or C atoms are dominated by the large repulsive Pauliexchange contributions, which in turn offsets the attractive interactions leading torelatively small binding energies. The Be2 dimer, e.g., after being doped inside a planarcarbon network, undergoes orbital adjustments due to charge transfer and unusualintermolecular interactions and is oriented perpendicular to the plane of the carbonnetwork with the Be−Be bond center located inside the plane. The present theoreticalinvestigation on the nature of bonding in C/Be2 exchange complexes, using state of theart quantum chemical techniques, reveals a sp2 carbon-like bonding scheme in Be2 arising due to the molecular hybridization of σand two π orbitals. The perturbations imposed by doped Be2 dimers exhibit a local character of the structural and electronicproperties of the complexes, and the separation by two carbon atoms between beryllium active centers is sufficient to considerthese centers as independent sites

Rapid and Accurate Prediction of pK(a) Values of C-H Acids Using Graph Convolutional Neural Networks

The ability to estimate the acidity of C-H groups within organic molecules in non-aqueous solvents is important in synthetic planning to correctly predict which protons will be abstracted in reactions such as alkylations, Michael additions, or aldol condensations. This Article describes the use of the so-called graph convolutional neural networks (GCNNs) to perform such predictions on the time scales of milliseconds and with accuracy comparing favorably with state-of-the-art solutions,. including commercial ones. The crux of the method is to train GCNNs using descriptors that reflect not only topological but also chemical properties of atomic environments. The model is validated against adversarial controls, supplemented by the discussion of realistic synthetic problems (on which it correctly predicts the most acidic protons in >90% of cases), and accompanied by a Web application intended to aid the community in everyday synthetic planning

Unique Bonding Nature of Carbon-Substituted Be₂ Dimer inside the Carbon (sp²) Network

Controlled doping of active carbon materials (viz., graphenes, carbon nanotubes etc.) may lead to the enhancement of their desired properties. The least studied case of C/Be substitution offers an attractive possibility in this respect. The interactions of Be₂ with Be or C atoms are dominated by the large repulsive Pauli exchange contributions, which in turn offsets the attractive interactions leading to relatively small binding energies. The Be₂ dimer, e.g., after being doped inside a planar carbon network, undergoes orbital adjustments due to charge transfer and unusual intermolecular interactions and is oriented perpendicular to the plane of the carbon network with the Be–Be bond center located inside the plane. The present theoretical investigation on the nature of bonding in C/Be₂ exchange complexes, using state of the art quantum chemical techniques, reveals a sp² carbon-like bonding scheme in Be₂ arising due to the molecular hybridization of σ and two π orbitals. The perturbations imposed by doped Be₂ dimers exhibit a local character of the structural and electronic properties of the complexes, and the separation by two carbon atoms between beryllium active centers is sufficient to consider these centers as independent sites

A computer algorithm to discover iterative sequences of organic reactions

Iterative syntheses comprise sequences of organic reactions in which the substrate molecules grow with each iteration and the functional groups, which enable the growth step, are regenerated to allow sustained cycling. Typically, iterative sequences can be automated, for example, as in the transformative examples of the robotized syntheses of peptides, oligonucleotides, polysaccharides and even some natural products. However, iterations are not easy to identify???in particular, for sequences with cycles more complex than protection and deprotection steps. Indeed, the number of catalogued examples is in the tens to maybe a hundred. Here, a computer algorithm using a comprehensive knowledge base of individual reactions constructs and evaluates myriads of putative, but chemically plausible, sequences and discovers an unprecedented number of iterative sequences. Some of these iterations are validated by experiment and result in the synthesis of motifs commonly found in natural products. This computer-driven discovery expands the pool of iterative sequences that may be automated in the future

Synthetic connectivity, emergence, and self-regeneration in the network of prebiotic chemistry

The challenge of prebiotic chemistry is to trace the syntheses of life's key building blocks from a handful of primordial substrates. Here we report a forward-synthesis algorithm that generates a full network of prebiotic chemical reactions accessible from these substrates under generally accepted conditions. This network contains both reported and previously unidentified routes to biotic targets, as well as plausible syntheses of abiotic molecules. It also exhibits three forms of nontrivial chemical emergence, as the molecules within the network can act as catalysts of downstream reaction types; form functional chemical systems, including self-regenerating cycles; and produce surfactants relevant to primitive forms of biological compartmentalization. To support these claims, computer-predicted, prebiotic syntheses of several biotic molecules as well as a multistep, self-regenerative cycle of iminodiacetic acid were validated by experiment

Delocalized, Asynchronous, Closed-Loop Discovery of Organic Laser Emitters

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 enables delocalized and asynchronous design–make–test–analyze cycles. We showcase 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 AI experiment planner, resulted in the discovery of 21 new state-of-the-art materials. Automated 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