For the investigation of chemical reaction networks, efficient and accurate determination all relevant intermediates elementary reactions is mandatory. The complexity such a network may grow rapidly, in particular if reactive species are involved that might cause myriad side reactions. Without automation, complete complex mechanisms tedious possibly unfeasible. Therefore, only expected dominant paths (e.g., catalytic cycle or an enzymatic cascade) usually explored practice. Here, we present...

Abstract Unlike 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 optimally utilized because selection conditions based on one‐variable‐at‐a‐time principle. We will summarize statistically motivated approaches already used context electroorganic synthesis. discuss central ideas these methods which originate from other fields chemistry relation...

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine corresponding free energy surface and solve resulting continuous-time rate equations for a continuous state space. general (complex) network, is computationally hard fulfill these two requirements. However, possible approximately address challenges physically consistent way. On one hand, may be sufficient consider approximate energies if reliable uncertainty...

We employ Gaussian process (GP) regression to adjust for systematic errors in D3-type dispersion corrections. refer the associated, statistically improved model as D3-GP. It is trained on differences between interaction energies obtained from PBE-D3(BJ)/ma-def2-QZVPP and DLPNO-CCSD(T)/CBS calculations. generated a data set containing 1248 molecular dimers, which resemble dispersion-dominated systems contained S66 set. Our represent not only equilibrium structures but also dimers with various...

One of the major challenges in computational science is to determine uncertainty a virtual measurement, that prediction an observable based on calculations. As highly accurate first-principles calculations are general unfeasible for most physical systems, one usually resorts parameteric property models observables, which require calibration by incorporating reference data. The resulting predictions and their uncertainties sensitive systematic errors such as inconsistent data, parametric...

We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semiaccurate but efficient electronic structure calculations. It is designed to (i) accelerate exploration such and (ii) cope with model-inherent errors in calculations on elementary steps. developed implemented KiNetX possess three features. First, evaluates relevance every species (yet incomplete) network confine search new steps only those that are considered...

Computational models in chemistry rely on a number of approximations. The effect such approximations observables derived from them is often unpredictable. Therefore, it challenging to quantify the uncertainty computational result, which, however, necessary assess suitability model. Common performance statistics as mean absolute error are prone failure they do not distinguish explainable (systematic) part errors their unexplainable (random) part. In this paper, we discuss problems and...

Nitrene transfer reactions catalyzed by heme proteins have broad potential for the stereoselective formation of carbon-nitrogen bonds. However, competition between productive nitrene and undesirable reduction precursors limits implementation such biocatalytic methods. Here, we investigated azides model protein myoglobin to gain mechanistic insights into factors that control fate key reaction intermediates. In this system, proceeds via a proposed intermediate is rapidly reduced protonated...

Semiclassical dispersion corrections developed by Grimme and co-workers have become indispensable in applications of Kohn–Sham density functional theory. A deeper understanding the underlying parametrization might be crucial for well-founded further improvements this successful approach. To end, we present an in-depth assessment fit parameters semiclassical (D3-type) means a statistically rigorous analysis. We find that choice cost function generally has small effect on empirical D3-type...

Abstract Single‐atom catalysts with iron ions in the active site, known as FeNC catalysts, show high activity for oxygen reduction reaction and hence hold promise access to low‐cost fuel cells. Because of amorphous, multiphase structure environment its electronic are poorly understood. While it is widely accepted that catalytically site contains an ion ligated by several nitrogen donors embedded a graphene‐like plane, exact structural details, such presence or nature axial ligands, unknown....

Selective and feasible reactions are among the top targets in synthesis planning. Mayr's approach to quantifying chemical reactivity has greatly facilitated planning process, but parameters for new compounds require time-consuming experiments. In past decade, data-driven modeling been gaining momentum field, as it shows promise terms of efficient prediction. However, state-of-the-art models use quantum data input, which prevent access real-time organic synthesis. Here, we present a novel...

Base-mediated C–H carboxylation is a versatile pathway for utilizing carbon dioxide (CO2) as C1 building block in organic synthesis. However, CO2 constitutes notorious thermodynamic sink, which restricts this approach to activated or intrinsically reactive nucleophiles. To qualitatively assess the stability of adducts, we present computational that integrates quantum chemistry with statistical modeling build predictive workflow. The target property affinity, specifically negative Gibbs free...

We present a data-driven approach that integrates supervised learning, quantum chemistry, and uncertainty quantification to determine CO 2 reactivity, enabling advances in carbon capture the design of value-added chemicals.

Experimental 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 conductance values. Modeling this experimental situation resulting histograms is challenging for theoretical methods, as computations need capture structural changes in experiments, including statistics formation rupture. This type extensive sampling implies that...

Carbon dioxide (CO2) can be transformed into valuable chemical building blocks, including C2-carboxylated 1,3-azoles, which have potential applications in pharmaceuticals, cosmetics, and pesticides. However, only a small fraction of the millions available 1,3-azoles are carboxylated at C2 position, highlighting significant opportunities for further research synthesis application these compounds. In this study, we utilized supervised machine learning approach to predict reaction yields data...

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, magnets potential application in information technology. We explore machine-learnability beyond linear regression, which has not been studied yet. employ Gaussian process since it can potentially deal with small training sets (as likely associated rather complex structures required exploring coupling) provides uncertainty...

Common trends in communication through molecular bridges are ubiquitous chemistry, such as the frequently observed exponential decay of conductance/electron transport and exchange spin coupling with increasing bridge length, or increased a upon closing diarylethene photoswitch. For antiferromagnetically coupled diradicals which two equivalent centers connected by closed‐shell bridge, orbitals (MOs) whose energy splitting dominates strength similar shape to MOs dithiolated bridges, turn can...

A series of selenophenes with redox-active amine end-capping groups was synthesized and investigated. combination cyclic voltammetry, optical absorption, EPR spectroscopy, quantum-chemical calculations based on Kohn-Sham density functional theory used to explore charge delocalization in the monocationic mixed-valence forms these selenophenes, results were compared those obtained from analogous studies structurally identical thiophenes. The striking finding is that comproportionation constant...

Bug tracking enables the monitoring and resolution of issues bugs within organizations. triaging, or assigning to owner(s) who will resolve them, is a critical component this process because there are many incorrect assignments that waste developer time reduce bug throughput. In work, we explore use novel two-output deep neural network architecture (Dual DNN) for triaging both an individual team developer, simultaneously. Dual DNN leverages simultaneous prediction by exploiting its own guess...

According to Mayr, polar organic synthesis can be rationalized by a simple empirical relationship linking bimolecular rate constants as few three reactivity parameters. Here, we propose an extension Mayr's method that is rooted in uncertainty quantification and transforms the parameters into probability distributions. Through propagation, these distributions transformed estimates for constants. Chemists exploit virtual error bars enhance planning decrease ambiguity of conclusions drawn from...