1,720,981 research outputs found
Quantitative Structure–Reactivity Relationships for Synthesis Planning: The Benzhydrylium Case
No full textCalibration of Computational Mössbauer Spectroscopy to Unravel Active Sites in FeNC-Catalysts for the Oxygen Reduction Reaction
No full textElectrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products
Full text linkUnlike common analytical techniques such as cyclic voltammetry, statistics-based optimization tools are not yet often in the toolbox of preparative organic electrochemists. In general, experimental effort is not optimally utilized because the selection of experimental conditions is based on the one-variable-at-a-time principle. We will summarize statistically motivated optimization approaches already used in the context of electroorganic synthesis. We discuss the central ideas of these optimization methods which originate from other fields of chemistry in relation to electrosynthetic applications
Electrosynthetic Screening and Modern Optimization Strategies for Electrosynthesis of Highly Value‐added Products
No full textTheoretical Studies of the Acid–Base Equilibria in a Model Active Site of the Human 20S Proteasome
No full textUncertainty Quantification of Reactivity Scales
No full textAbstract According to Mayr, polar organic synthesis can be rationalized by a simple empirical relationship linking bimolecular rate constants to as few as three reactivity parameters. Here, we propose an extension to Mayr's reactivity method that is rooted in uncertainty quantification and transforms the reactivity parameters into probability distributions. Through uncertainty propagation, these distributions can be transformed into uncertainty estimates for bimolecular rate constants. Chemists can exploit these virtual error bars to enhance synthesis planning and to decrease the ambiguity of conclusions drawn from experimental data. We demonstrate the above at the example of the reference data set released by Mayr and co‐workers [J. Am. Chem. Soc. 2001, 123, 9500; J. Am. Chem. Soc. 2012, 134, 13902]. As by‐product of the new approach, we obtain revised reactivity parameters for 36 π‐nucleophiles and 32 benzhydrylium ions.Reliable uncertainty! We propose an extension to Mayr's reactivity scale method that is rooted in uncertainty quantification. It yields reliable uncertainty estimates for bimolecular rate constants. Chemists can exploit these virtual error bars to enhance synthesis planning and to decrease the ambiguity of conclusions drawn from experimental data (image by Prof. Ricardo A. Mata). imageGerman Research Foundation (DFG
Learning Conductance: Gaussian Process Regression for Molecular Electronics
No full textExperimental
studies of charge transport through single
molecules
often rely on break junction setups, where molecular junctions are
repeatedly formed and broken while measuring the conductance, leading
to a statistical distribution of conductance values. Modeling this
experimental situation and the resulting conductance histograms is
challenging for theoretical methods, as computations need to capture
structural changes in experiments, including the statistics of junction
formation and rupture. This type of extensive structural sampling
implies that even when evaluating conductance from computationally
efficient electronic structure methods, which typically are of reduced
accuracy, the evaluation of conductance histograms is too expensive
to be a routine task. Highly accurate quantum transport computations
are only computationally feasible for a few selected conformations
and thus necessarily ignore the rich conformational space probed in
experiments. To overcome these limitations, we investigate the potential
of machine learning for modeling conductance histograms, in particular
by Gaussian process regression. We show that by selecting specific
structural parameters as features, Gaussian process regression can
be used to efficiently predict the zero-bias conductance from molecular
structures, reducing the computational cost of simulating conductance
histograms by an order of magnitude. This enables the efficient calculation
of conductance histograms even on the basis of computationally expensive
first-principles approaches by effectively reducing the number of
necessary charge transport calculations, paving the way toward their
routine evaluation
- …
