Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability.

Activation energy characterization of competing reactions is a costly but crucial step for understanding the kinetic relevance of distinct reaction pathways, product yields, and myriad other properties of reacting systems. The standard methodology for activation energy characterization has historically been a transition state search using the highest level of theory that can be afforded. However, recently, several groups have popularized the idea of predicting activation energies directly based on nothing more than the reactant and product graphs, a sufficiently complex neural network, and a broad enough data set. Here, we have revisited this task using the recently developed Reaction Graph Depth 1 (RGD1) transition state data set and several newly developed graph attention architectures. All of these new architectures achieve similar state-of-the-art results of ∼4 kcal/mol mean absolute error on withheld testing sets of reactions but poor performance on external testing sets composed of reactions with differing mechanisms, reaction molecularity, or reactant size distribution. Limited transferability is also shown to be shared by other contemporary graph to activation energy architectures through a series of case studies. We conclude that an array of standard graph architectures can already achieve results comparable to the irreducible error of available reaction data sets but that out-of-distribution performance remains poor.

Reductive Doping Inhibits the Formation of Isomerization-Derived Structural Defects in N-doped Poly(benzodifurandione) (n-PBDF).

Recently, solution-processable n-doped poly(benzodifurandione) (n-PBDF) has been made through in-situ oxidative polymerization and reductive doping, which exhibited exceptionally high electrical conductivities and optical transparency. The discovery of n-PBDF is considered a breakthrough in the field of organic semiconductors. In the initial report, the possibility of structural defect formation in n-PBDF was proposed, based on the observation of structural isomerization from (E)-2H,2'H-[3,3'-bibenzofuranylidene]-2,2'-dione (isoxindigo) to chromeno[4,3-c]chromene-5,11-dione (dibenzonaphthyrone) in the dimer model reactions. In this study, we present clear evidence that structural isomerization is inhibited during polymerization. We reveal that the dimer (BFD1) and the trimer (BFD2) can be reductively doped by several mechanisms, including hydride transfer, forming charge transfer complexes (CTC) or undergoing an integer charge transfer (ICT) with reactants available during polymerization. Once the hydride transfer adducts, the CTC, or the ICT product forms, structural isomerization can be effectively prevented even at elevated temperatures. Our findings provide a mechanistic understanding of why isomerization-derived structural defects are absent in n-PBDF backbone. It lays a solid foundation for the future development of n-PBDF as a benchmark polymer for organic electronics and beyond.

Deductive Machine Learning Challenges and Opportunities in Chemical Applications.

Contemporary machine learning algorithms have largely succeeded in automating the development of mathematical models from data. Although this is a striking accomplishment, it leaves unaddressed the multitude of scenarios, especially across the chemical sciences and engineering, where deductive, rather than inductive, reasoning is required and still depends on manual intervention by an expert. This review describes the characteristics of deductive reasoning that are helpful for understanding the role played by expert intervention in problem-solving and explains why such interventions are often relatively resistant to disruption by typical machine learning strategies. The article then discusses the factors that contribute to creating a deductive bottleneck, how deductive bottlenecks are currently addressed in several application areas, and how machine learning models capable of deduction can be designed. The review concludes with a tutorial case study that illustrates the challenges of deduction problems and a notebook for readers to experiment with on their own.

Deductive machine learning models for product identification.

Deductive solution strategies are required in prediction scenarios that are under determined, when contradictory information is available, or more generally wherever one-to-many non-functional mappings occur. In contrast, most contemporary machine learning (ML) in the chemical sciences is inductive learning from example, with a fixed set of features. Chemical workflows are replete with situations requiring deduction, including many aspects of lab automation and spectral interpretation. Here, a general strategy is described for designing and training machine learning models capable of deduction that consists of combining individual inductive models into a larger deductive network. The training and testing of these models is demonstrated on the task of deducing reaction products from a mixture of spectral sources. The resulting models can distinguish between intended and unintended reaction outcomes and identify starting material based on a mixture of spectral sources. The models also perform well on tasks that they were not directly trained on, like performing structural inference using real rather than simulated spectral inputs, predicting minor products from named organic chemistry reactions, identifying reagents and isomers as plausible impurities, and handling missing or conflicting information. A new dataset of 1 124 043 simulated spectra that were generated to train these models is also distributed with this work. These findings demonstrate that deductive bottlenecks for chemical problems are not fundamentally insuperable for ML models.

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.

Molecular templating of layered halide perovskite nanowires.

Layered metal-halide perovskites, or two-dimensional perovskites, can be synthesized in solution, and their optical and electronic properties can be tuned by changing their composition. We report a molecular templating method that restricted crystal growth along all crystallographic directions except for [110] and promoted one-dimensional growth. Our approach is widely applicable to synthesize a range of high-quality layered perovskite nanowires with large aspect ratios and tunable organic-inorganic chemical compositions. These nanowires form exceptionally well-defined and flexible cavities that exhibited a wide range of unusual optical properties beyond those of conventional perovskite nanowires. We observed anisotropic emission polarization, low-loss waveguiding (below 3 decibels per millimeter), and efficient low-threshold light amplification (below 20 microjoules per square centimeter).

Simultaneously improving reaction coverage and computational cost in automated reaction prediction tasks.

Automated reaction prediction has the potential to elucidate complex reaction networks for applications ranging from combustion to materials degradation, but computational cost and inconsistent reaction coverage are still obstacles to exploring deep reaction networks. Here we show that cost can be reduced and reaction coverage can be increased simultaneously by relatively straightforward modifications of the reaction enumeration, geometry initialization and transition state convergence algorithms that are common to many prediction methodologies. These components are implemented in the context of yet another reaction program (YARP), our reaction prediction package with which we report reaction discovery benchmarks for organic single-step reactions, thermal degradation of a γ-ketohydroperoxide, and competing ring-closures in a large organic molecule. Compared with recent benchmarks, YARP (re)discovers both established and unreported reaction pathways and products while simultaneously reducing the cost of reaction characterization by nearly 100-fold and increasing convergence of transition states. This combination of ultra-low cost and high reaction coverage creates opportunities to explore the reactivity of larger systems and more complex reaction networks for applications such as chemical degradation, where computational cost is a bottleneck.