The stabilization of silicon(II) and germanium(II) dihydrides by an intramolecular Frustrated Lewis Pair (FLP) ligand, PB, i Pr 2 P(C 6 H 4)BCy 2 (Cy = cyclohexyl) is reported. The resulting hydride complexes [PB{SiH 2 }] and [PB{GeH 2 }] are indefinitely stable at room temperature, yet can deposit films of silicon and germanium, respectively, upon mild thermolysis in solution. Hallmarks of this work include: 1) the ability to recycle the FLP phosphine-borane ligand (PB) after element deposition, and 2) the single-source precursor [PB{SiH 2 }] deposits Si films at a record low temperature from solution (110 8C). The dialkylsilicon(II) adduct [PB{SiMe 2 }] was also prepared, and shown to release poly(dimethylsilane) [SiMe 2 ] n upon heating. Overall, this study introduces a "closed loop" deposition strategy for semiconductors that steers materials science away from the use of harsh reagents or high temperatures. Scheme 1. General concept of FLP-assisted semiconductor (E) and polymer [ER 2 ] n deposition (E = Si, Ge).

Accurately predicting new polymers' properties with machine learning models apriori to synthesis has potential to significantly accelerate new polymers' discovery and development. However, accurately and efficiently capturing polymers' complex, periodic structures in machine learning models remains a grand challenge for the polymer cheminformatics community. Specifically, there has yet to be an ideal solution for the problems of how to capture the periodicity of polymers, as well as how to optimally develop polymer descriptors without requiring human-based feature design. In this work, we tackle these problems by utilizing a periodic polymer graph representation that accounts for polymers' periodicity and coupling it with a message-passing neural network that leverages the power of graph deep learning to automatically learn chemically relevant polymer descriptors. Remarkably, this approach achieves state-of-the-art performance on 8 out of 10 distinct polymer property prediction tasks. These results highlight the advancement in predictive capability that is possible through learning descriptors that are specifically optimized for capturing the unique chemical structure of polymers.

A new, base-free high turnover number (TON) catalyst for hydrogenation of simple and functionalized amides is prepared by reacting [Ru(η3-C3H5)(Ph2P(CH2)2NH2)2]BF4 and BH4− under hydrogen.

Two-dimensional (2D) materials derived from van der Waals (vdW)-bonded layered crystals have been the subject of considerable research focus, but their one-dimensional (1D) analogues have received less attention. These bulk crystals consist of covalently bonded multiatom atomic chains with weak van der Waals bonds between adjacent chains. Using density-functional-theory-based methods, we find the binding energies of several 1D families of materials to be within typical exfoliation ranges possible for 2D materials. In addition, we compute the electronic properties of a variety of insulating, semiconducting, and metallic individual wires and find differences that could enable the identification of and distinction between 1D, 2D, and 3D forms during mechanical exfoliation onto a substrate. We find 1D wires from chemical families of the forms PdBr 2 , SbSeI, and GePdS 3 are likely to be distinguishable from bulk materials via photoluminescence. Like 2D vdW materials, we find some of these 1D vdW materials have the potential to retain their bulk properties down to nearly atomic film thicknesses, including the structural families of HfI 3 and PNF 2 , a useful property for some applications including electronic interconnects. We also study naturally occurring bulk crystalline heterostructures of 1D wires and identify two families that are likely to be exfoliable and identifiable as individual 1D wire subcomponents.

We discover the chemical composition of over 1000 materials that are likely to exhibit layered and 2D phases but have yet to be synthesized. This includes two materials our calculations indicate can exist in distinct structures with different band gaps, expanding the short list of 2D phase-change materials. Whereas databases of over 1000 layered materials have been reported, we provide the first full database of materials that are likely layered but are yet to be synthesized, providing a roadmap for the synthesis community. We accomplish this by combining physics with machine learning on experimentally obtained data and verify a subset of candidates using density functional theory. We find that our model performs five times better than practitioners in the field at identifying layered materials and is comparable to or better than professional solid-state chemists. Finally, we find that semisupervised learning can offer benefits for materials design where labels for some of the materials are unknown.

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The high brightness, low emittance electron beams achieved in modern X‐ray free‐electron lasers (XFELs) have enabled powerful X‐ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub‐nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial‐and‐error‐based iterative approaches, this work represents the first data‐driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M2O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state‐of‐the‐art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.

The stabilization of silicon(II) and germanium(II) dihydrides by an intramolecular Frustrated Lewis Pair (FLP) ligand, PB, i Pr 2 P(C 6 H 4)BCy 2 (Cy = cyclohexyl) is reported. The resulting hydride complexes [PB{SiH 2 }] and [PB{GeH 2 }] are indefinitely stable at room temperature, yet can deposit films of silicon and germanium, respectively, upon mild thermolysis in solution. Hallmarks of this work include: 1) the ability to recycle the FLP phosphine-borane ligand (PB) after element deposition, and 2) the single-source precursor [PB{SiH 2 }] deposits Si films at a record low temperature from solution (110 8C). The dialkylsilicon(II) adduct [PB{SiMe 2 }] was also prepared, and shown to release poly(dimethylsilane) [SiMe 2 ] n upon heating. Overall, this study introduces a "closed loop" deposition strategy for semiconductors that steers materials science away from the use of harsh reagents or high temperatures. Scheme 1. General concept of FLP-assisted semiconductor (E) and polymer [ER 2 ] n deposition (E = Si, Ge).