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

Paramagnetic GaN:Fe and ferromagnetic (Ga,Fe)N - relation between structural, electronic, and magnetic properties

We report on the metalorganic chemical vapor deposition (MOCVD) of GaN:Fe and (Ga,Fe)N layers on c-sapphire substrates and their thorough characterization via high-resolution x-ray diffraction (HRXRD), transmission electron microscopy (TEM), spatially-resolved energy dispersive X-ray spectroscopy (EDS), secondary-ion mass spectroscopy (SIMS), photoluminescence (PL), Hall-effect, electron-paramagnetic resonance (EPR), and magnetometry employing a superconducting quantum interference device (SQUID). A combination of TEM and EDS reveals the presence of coherent nanocrystals presumably FexN with the composition and lattice parameter imposed by the host. From both TEM and SIMS studies, it is stated that the density of nanocrystals and, thus the Fe concentration increases towards the surface. In layers with iron content x<0.4% the presence of ferromagnetic signatures, such as magnetization hysteresis and spontaneous magnetization, have been detected. We link the presence of ferromagnetic signatures to the formation of Fe-rich nanocrystals, as evidenced by TEM and EDS studies. This interpretation is supported by magnetization measurements after cooling in- and without an external magnetic field, pointing to superparamagnetic properties of the system. It is argued that the high temperature ferromagnetic response due to spinodal decomposition into regions with small and large concentration of the magnetic component is a generic property of diluted magnetic semiconductors and diluted magnetic oxides showing high apparent Curie temperature.Comment: 21 pages, 30 figures, submitted to Phys. Rev.

Stable topological insulators achieved using high energy electron beams

Topological insulators are potentially transformative quantum solids with metallic surface states which have Dirac band structure and are immune to disorder. Ubiquitous charged bulk defects, however, pull the Fermi energy into the bulk bands, denying access to surface charge transport. Here we demonstrate that irradiation with swift (B2.5MeV energy) electron beams allows to compensate these defects, bring the Fermi level back into the bulk gap and reach the charge neutrality point (CNP). Controlling the beam fluence, we tune bulk conductivity from p- (hole-like) to n-type (electron-like), crossing the Dirac point and back, while preserving the Dirac energy dispersion. The CNP conductance has a two-dimensional character on the order of ten conductance quanta and reveals, both in Bi2Te3 and Bi2Se3, the presence of only two quantum channels corresponding to two topological surfaces. The intrinsic quantum transport of the topological states is accessible disregarding the bulk size

13. Freiburger Symposium 2017: Green Chemistry –from Concept to Industrial Reality(Synthetic Design with the Chematica Program – The Importance of Accurate Rules and of Higherorder Logic)

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

The challenge of prebiotic chemistry is to trace the syntheses of life&apos;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