Structural Investigation of New Lithium Amidinates and Guanidinates

A series of potentially useful lithium amidinates and guanidinates were prepared and fully characterized. Treatment of N,N′‐diisopropylcarbodiimide with phenyllithium in diethyl ether afforded the lithium amidinate [PhC(NiPr)₂Li(OEt₂)]₂ ( 1 ). Similar treatment of N,N′‐diorganocarbodiimides R′–N=C=N–R′ [R′ = iPr, cyclohexyl (Cy)] with secondary lithium amides LiNR₂ [R₂ = Et₂, iPr₂, (CH₂)₄] followed by crystallization from THF or 1,4‐dioxane gave the lithium guanidinates [R₂NC(NR′)₂Li(S)]₂ [ 2 : R = Et, R′ = iPr, S = THF; 3 : R₂ = (CH₂)₄, R′ = iPr, S = THF; 4 : R = R′ = iPr, S = ½ 1,4‐dioxane; 5 : R₂ = (CH₂)₄, R′ = Cy, S = 1,4‐dioxane] as crystalline solids. Reaction of N‐lithioaziridine with the corresponding carbodiimides afforded solvent‐deficient [{C₂H₄NC(NiPr₂)₂}₂Li₂(THF)]₂ ( 6 ), and [C₂H₄NC(NEt)(NtBu)Li(THF)]₂ ( 7 ). Crystal structure determination revealed the presence of common ladder‐type dimeric structures for 1 – 5 . Compound 6 exists as a dimer of two ladder‐type dimers in the crystal, and 7 exhibits an unusual dimeric structure comprising an eight‐membered C₂N₄Li₂ ring.     

Harmonic maps with torsion

In this article we introduce a natural extension of the well-studied equation for harmonic maps between Riemannian manifolds by assuming that the target manifold is equipped with a connection that is metric but has non-vanishing torsion. Such connections have already been classified in the work of Cartan(1924).The maps under consideration do not arise as critical points of an energy functional leading to interesting mathematical challenges. We will perform a first mathematical analysis of these maps which we will call harmonic maps with torsion.     

Hierarchically Structured Allotropes of Phosphorus from Data-Driven Exploration

The discovery of materials is increasingly guided by quantum-mechanical crystal-structure prediction, but the structural complexity in bulk and nanoscale materials remains a bottleneck. Here we demonstrate how data-driven approaches can vastly accelerate the search for complex structures, combining a machine-learning (ML) model for the potential-energy surface with efficient, fragment-based searching. We use the characteristic building units observed in Hittorf's and fibrous phosphorus to seed stochastic (“random”) structure searches over hundreds of thousands of runs. Our study identifies a family of hierarchically structured allotropes based on a P8 cage as principal building unit, including one-dimensional (1D) single and double helix structures, nanowires, and two-dimensional (2D) phosphorene allotropes with square-lattice and kagome topologies. These findings yield new insight into the intriguingly diverse structural chemistry of phosphorus, and they provide an example for how ML methods may, in the long run, be expected to accelerate the discovery of hierarchical nanostructures.