Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the Corey-Bakshi-Shibata Reduction.

We present a case study on how to improve an existing metal-free catalyst for a particularly difficult reaction, namely, the Corey-Bakshi-Shibata (CBS) reduction of butanone, which constitutes the classic and prototypical challenge of being able to differentiate a methyl from an ethyl group. As there are no known strategies on how to address this challenge, we leveraged the power of machine learning by constructing a realistic (for a typical laboratory) small, albeit high-quality, data set of about 100 reactions (run in triplicate) that we used to train a model in combination with a key-intermediate graph (of substrate and catalyst) to predict the differences in Gibbs activation energies ΔΔG‡ of the enantiomeric reaction paths. With the help of this model, we were able to select and subsequently screen a small selection of catalysts and increase the selectivity for the CBS reduction of butanone to 80% enantiomeric excess (ee), the highest possible value achieved to date for this substrate with a metal-free catalyst, thereby also exceeding the best available enzymatic systems (64% ee) and the selectivity with Corey's original catalyst (60% ee). This translates into a >50% improvement in relative ΔG‡ from 0.9 to 1.4 kcal mol-1. We underscore the transformative potential of machine learning in accelerating catalyst design because we rely on a manageable small data set and a key-intermediate graph representing a combination of catalyst and substrate graphs in lieu of a transition-state model. Our results highlight the synergy of synthetic chemistry and data-centric approaches and provide a blueprint for future catalyst optimization.

Machine Learning for Bridging the Gap between Density Functional Theory and Coupled Cluster Energies.

Accurate electronic energies and properties are crucial for successful reaction design and mechanistic investigations. Computing energies and properties of molecular structures has proven extremely useful, and, with increasing computational power, the limits of high-level approaches (such as coupled cluster theory) are expanding to ever larger systems. However, because scaling is highly unfavorable, these methods are still not universally applicable to larger systems. To address the need for fast and accurate electronic energies of larger systems, we created a database of around 8000 small organic monomers (2000 dimers) optimized at the B3LYP-D3(BJ)/cc-pVTZ level of theory. This database also includes single-point energies computed at various levels of theory, including PBE1PBE, ωΒ97Χ, M06-2X, revTPSS, B3LYP, and BP86, for density functional theory as well as DLPNO-CCSD(T) and CCSD(T) for coupled cluster theory, all in conjunction with a cc-pVTZ basis. We used this database to train machine learning models based on graph neural networks using two different graph representations. Our models are able to make energy predictions from B3LYP-D3(BJ)/cc-pVTZ inputs to CCSD(T)/cc-pVTZ outputs with a mean absolute error of 0.78 and to DLPNO-CCSD(T)/cc-pVTZ with an mean absolute error of 0.50 and 0.18 kcal mol-1 for monomers and dimers, respectively. The model for dimers was further validated on the S22 database, and the monomer model was tested on challenging systems, including those with highly conjugated or functionally complex molecules.

Machine Learning of Coupled Cluster (T)-Energy Corrections via Delta (Δ)-Learning.

Accurate thermochemistry is essential in many chemical disciplines, such as astro-, atmospheric, or combustion chemistry. These areas often involve fleetingly existent intermediates whose thermochemistry is difficult to assess. Whenever direct calorimetric experiments are infeasible, accurate computational estimates of relative molecular energies are required. However, high-level computations, often using coupled cluster theory, are generally resource-intensive. To expedite the process using machine learning techniques, we generated a database of energies for small organic molecules at the CCSD(T)/cc-pVDZ, CCSD(T)/aug-cc-pVDZ, and CCSD(T)/cc-pVTZ levels of theory. Leveraging the power of deep learning by employing graph neural networks, we are able to predict the effect of perturbatively included triples (T), that is, the difference between CCSD and CCSD(T) energies, with a mean absolute error of 0.25, 0.25, and 0.28 kcal mol-1 (R2 of 0.998, 0.997, and 0.998) with the cc-pVDZ, aug-cc-pVDZ, and cc-pVTZ basis sets, respectively. Our models were further validated by application to three validation sets taken from the S22 Database as well as to a selection of known theoretically challenging cases.

London Dispersion Helps Refine Steric A-Values: Dispersion Energy Donor Scales.

We suggest a scale of dispersion energy donors (DEDs) that allows for direct comparisons with steric effects. This scale is based on the classic A-values and allows groups to reorient to minimize strain, thereby providing an advantage over raw group polarizabilities. The A-value can no longer be considered purely a steric factor. Even for groups that do not participate in charge transfer or electrostatic interactions, the A-value includes Pauli repulsion (steric hindrance) and attractive London dispersion (LD) interactions. Although the common assumption is that, at the distances found in monosubstituted cyclohexanes, steric demands are the key factors influencing conformer preferences, we show in this computational study that there is a non-negligible LD part. We use this system to build a DED scale and a complementary steric scale. These scales are quantitatively comparable, as they are based on the same system, and allow for comparison of the two competing interactions in experimentally relevant settings. In addition, we show that LD interactions can be used to explain puzzling data regarding relative group sizes.

Aminohydroxymethylene (H2N-C̈-OH), the Simplest Aminooxycarbene.

We generated and isolated hitherto unreported aminohydroxymethylene (1, aminohydroxycarbene) in solid Ar via pyrolysis of oxalic acid monoamide (2). Astrochemically relevant carbene 1 is persistent under cryogenic conditions and only decomposes to HNCO + H2 and NH3 + CO upon irradiation of the matrix at 254 nm. This photoreactivity is contrary to other hydroxycarbenes and aminomethylene, which undergo [1,2]H shifts to the corresponding carbonyls or imine. The experimental data are well supported by the results of CCSD(T)/cc-pVTZ and B3LYP/6-311++G(3df,3pd) computations.

London Dispersion Helps Refine Steric A-Values: The Halogens.

Halogens are rarely considered as dispersion energy donors for organic reaction design. Here, we re-examine one of the textbook examples for assessing steric hindrance, the A-value, and demonstrate that even in this system, halogens cannot be treated solely as classic repulsive hard spheres. A significant part of the steric demand of the halogens is compensated by attractive London dispersion (LD) interactions, explaining the experimental lack of a clear trend when going down the halogens' row. Beyond monohalogenated cyclohexanes, dihalo- and perhalocyclohexanes also show significant LD interactions. We also explored several other small organic systems containing halogens. Our findings show that organic chemists should treat halogens as possible sources of LD interactions in reaction design, as these atoms can change the landscape of the potential energy surface and reverse trends of conformer stabilities and reaction selectivities.

Ethynylhydroxycarbene (H-C≡C-C̈-OH).

The species on the C3H2O potential energy surface have long been known to play a vital role in extraterrestrial chemistry. Here we report on the hitherto uncharacterized isomer ethynylhydroxycarbene (H-C≡C-C̈-OH, 1) generated by high-vacuum flash pyrolysis of ethynylglyoxylic acid ethyl ester and trapped in solid argon matrices at 3 and 20 K. Upon irradiation at 436 nm trans-1 rearranges to its higher lying cis-conformer. Prolonged irradiation leads to the formation of propynal. When the matrix is kept in the dark, 1 reacts within a half-life of ca. 70 h to propynal in a conformer-specific [1,2]H-tunneling process. Our results are fully consistent with computations at the CCSD(T)/cc-pVTZ and the B3LYP/def2-QZVPP levels of theory.

Blue-green emitting microdisks using low-temperature-grown ZnO on patterned silicon substrates.

Zinc oxide (ZnO) as an extremely bright emitter is an attractive material for photonic devices. However, devices made of epitaxially grown ZnO are difficult to fabricate due to the lack of selective etching processes. Here, we demonstrate that by a low-temperature growth process on pre-patterned silicon dioxide (SiO2) microdisks (MDs) high quality ZnO resonators are created. The devices exhibit whispering gallery modes (WGMs) over the blue-green part of the visible spectrum with quality factors exceeding Q = 3500, which are among the highest values reported in this material system so far. By deposition of SiO2 capping layers we find an enhanced coupling of the spontaneous emission from the active medium into the MDs, observed by sharp WGMs up to a radial quantum number of N = 3.