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Four unsaturated poly(carbooligosilane)s (P1–P4) were prepared via acyclic diene metathesis polycondensation of new oligosilane diene monomers (1–4). These novel polymers with varying main-chain Si incorporation have high trans internal olefin stereochemistry (ca. 80%) and molecular weights (9500–21,700 g mol–1). Postpolymerization epoxidation converted all alkene moieties to epoxides and rendered the polymers (P5–P8) more electrophilic, which allowed for single-molecule force spectroscopy studies via a modified atomic force microscope setup with a silicon tip and cantilever. The single-chain elasticity of the polycarbooligosilanes decreased with increasing numbers of Si–Si bonds, a finding reproduced by quantum chemical calculations. 

Two distinct N-containing POCs show exceptional Br₂vapor adsorption ability and different reversibility of adsorption by forming charge-transfer complexes with polybromide species, as demonstrated by experimental investigations and DFT calculations.

 

The cis- and trans-isomers of a silacycloheptene were selectively synthesized by the alkylation of a silyl dianion, a novel approach to strained cycloalkenes. The trans-silacycloheptene (trans-SiCH) was significantly more strained than the cis isomer, as predicted by quantum chemical calculations and confirmed by crystallographic signatures of a twisted alkene. Each isomer exhibited distinct reactivity toward ring-opening metathesis polymerization (ROMP), where only trans-SiCH afforded high-molar-mass polymer under enthalpy-driven ROMP. Hypothesizing that the introduction of silicon might result in increased molecular compliance at large extensions, we compared poly(trans-SiCH) to organic polymers by single-molecule force spectroscopy (SMFS). Force-extension curves from SMFS showed that poly(trans-SiCH) is more easily overstretched than two carbon-based analogues, polycyclooctene and polybutadiene, with stretching constants that agree well with the results of computational simulations. 

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.