Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations.

In organic reactivity studies, quantum chemical calculations play a pivotal role as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g., CCSD(T)) have significantly advanced our understanding of chemical reactivity, they frequently fall short in describing multiconfigurational transition states and intermediates. Achieving a more accurate description necessitates the use of multireference methods. However, these methods have not been used at scale due to their often-faulty predictions without expert input. Here, we overcome this deficiency with automated multiconfigurational pair-density functional theory (MC-PDFT) calculations. We apply this method to 908 automatically generated organic reactions. We find 68% of these reactions present significant multiconfigurational character in which the automated multiconfigurational approach often provides a more accurate and/or efficient description than DFT and CCSD(T). This work presents the first high-throughput application of automated multiconfigurational methods to reactivity, enabled by automated active space selection algorithms and the computation of electronic correlation with MC-PDFT on-top functionals. This approach can be used in a black-box fashion, avoiding significant active space inconsistency error in both single- and multireference cases and providing accurate multiconfigurational descriptions when needed.

Reactive Vapor-Phase Inhibitors for Area-Selective Depositions at Tunable Critical Dimensions.

Area-selective depositions (ASD) take advantage of the chemical contrast between material surfaces in device fabrication, where a film can be selectively grown by chemical vapor deposition on metal versus a dielectric, for instance, and can provide a path to nontraditional device architectures as well as the potential to improve existing device fabrication schemes. While ASD can be accessed through a variety of methods, the incorporation of reactive moieties in inhibitors presents several advantages, such as increasing thermal stability and limiting precursor diffusion into the blocking layer. Alkyne-terminated small molecule inhibitors (SMIs)─propargyl, dipropargyl, and tripropargylamine─were evaluated as metal-selective inhibitors. Modeling these SMIs provided insight into the binding mechanism, influence of sterics, and complex polymer network formed from the reaction between inhibitors consisting of alkene, aromatic, and network branchpoints. While a significant contrast in the binding of the SMIs on copper versus a dielectric was observed, residual amounts were detected on the dielectric surfaces, leading to variable ALD growth rates dependent on pattern-critical dimensions. This behavior can be controlled and utilized to direct film growth on patterns only above a critical threshold dimension; below this threshold, both the dielectric and metal features are protected. This method provides another design parameter for ASD processes and may extend its application to broader-ranging device fabrication schemes.

Comprehensive exploration of graphically defined reaction spaces.

Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.