Andrew Tarzia

Pd(II)-mediated isomerization enables self-assembly of both cis- and trans -ligand L into a discrete double-walled triangular trans- [Pd ₃ L ₆ ] ⁶⁺  metallacycle.

Despite advances in crystal engineering, predicting the packing of metal-containing molecules remains a significant challenge due to their increased structural complexity and computational cost relative to purely organic compounds. Yet, predicting solid-state structures is critical for understanding structureproperty relationships, particularly in modular porous systems such as metal–organic cages (MOCs), where packing governs access to the internal pores and thereby impacts functionality. Here, we present a computationally inexpensive methodology that combines semi-empirical dimer calculations with coarse-grained modelling to predict the packing behaviour of MOCs. This approach enables a priori prediction of the solid state and allows insight into how molecular shape and intermolecular interactions influence the crystalline phase behaviour of MOCs. Our findings provide a foundation for the rational design of MOCs with tailored solid-state structures and properties.

Knotting and weaving, at both macroscopic and molecular scales, play an essential role in determining the physical properties of materials. While classical crossing points in molecular knots have been extensively studied, synthetic bifurcated knots, containing characteristic junctions where a strand divides into two branches, represent an underexplored class of topologically complex molecular knots. Although reports of such structures are rare due to synthetic challenges, natural bifurcated knots have been identified in proteins and linked to potentially enhancing protein robustness. Here we report the synthesis of a 524-atom bifurcated knot featuring 3 classical crossings and 20 bifurcated junctions via 2 sequential, selective, imine condensations. First, Zn ^II ions are used to template the subcomponent self-assembly of a Zn ^II ₈ L ₆ architecture, with three unreacted aldehyde groups protruding from each of its eight vertices. Second, a geometry-matching tris-aniline condenses with these terminal aldehydes, yielding a covalently linked bifurcated knot with enhanced robustness, as quantified using collision-induced dissociation mass spectrometry. We anticipate that this approach will facilitate the design of new mechanically interlocked molecules and highly entangled molecular materials with increased robustness.

Cage structure prediction has made significant strides by generating structures based on what the community has seen before. However, to computationally design and discover novel structures, the community must be able to evaluate and model all structural candidates. Here, we introduce unbiased structure prediction workflows in our software, cgx , facilitated by exploration algorithms and our low‐cost minimal models. By comparing to experiments, we show that our approach predicts cage structures starting only from the experimental inputs (building block types, features and their stoichiometry). We demonstrate the use of this method prior to any costly experimental commitment, providing an efficient automated approach that is open source and applicable to multiple model resolutions. By providing recipes with copyable code in the documentation, we make uptake of this new method as facile as possible for chemists with a wide‐range of expertise.

Cage structure prediction has made significant strides by generating structures based on what the community has seen before. However, to computationally design and discover novel structures, the community must be able to evaluate and model all structural candidates. Here, we introduce unbiased structure prediction workflows in our software, cgx , facilitated by exploration algorithms and our low‐cost minimal models. By comparing to experiments, we show that our approach predicts cage structures starting only from the experimental inputs (building block types, features and their stoichiometry). We demonstrate the use of this method prior to any costly experimental commitment, providing an efficient automated approach that is open source and applicable to multiple model resolutions. By providing recipes with copyable code in the documentation, we make uptake of this new method as facile as possible for chemists with a wide‐range of expertise.

The study of complex many-body systems via analysis of the trajectories of the units that dynamically move and interact within them is a non-trivial task. The workflow for extracting meaningful information from the raw trajectory data is often composed of a series of interconnected steps, such as (i) identifying and tracking the constitutive objects/particles, resolving their trajectories (e.g., in experimental cases, where these are not automatically available as in typical molecular simulations); (ii) translating the trajectories into data that are easier to handle/analyze by using well-suited descriptors; and (iii) extracting meaningful information from such data. Each of these different tasks often requires non-negligible programming skills, the use of various types of representations or methods, and the availability/development of an interface between them. Despite the considerable potential that new tools contributed to each of these individual steps, their integration under a common framework would decrease the barrier to usage (especially by diverse communities of users), avoid fragmentation, and ultimately facilitate the development of new approaches in data analysis. To this end, here we introduce dynsight, an open Python platform that streamlines the extraction and analysis of time-series data from simulation or experimentally resolved trajectories. dynsight simplifies workflows, enhances accessibility, and facilitates time-series and trajectories data analysis, offering a useful tool for unraveling the dynamic complexity of a variety of systems (or signals) across different scales. dynsight is open source (github.com/GMPavanLab/dynsight) and can be easily installed using pip.

Current methods for predicting molecular porous materials typically exploit prior knowledge of similar systems, which biases the final outcome to a limited exploration space. To design novel structures and materials, the community must be able to evaluate and model all candidates without any bias. In this paper, we introduce blind structure prediction workflows into our software cgx, taking advantage of (but not limited to) low-cost models. We developed exploration algorithms that learn on-the-fly to not naively evaluate every structure and avoid wasted computational cost. With a direct comparison to experiments, we show that our approach predicts existing cage structures starting only from the experimental inputs (building blocks and their stoichiometry). We demonstrate how using this method prior to any costly experimental commitment would be useful, providing an efficient automated approach that is open source and applicable to any model resolution.

Despite advances in crystal engineering, predicting the packing of metal-containing molecules remains a significant challenge due to their increased structural complexity and computational cost relative to purely organic compounds. Yet, predicting solid-state structures is critical for understanding structure-property relationships, particularly in modular porous systems such as metal--organic cages (MOCs), where packing governs access to the internal pores and thereby impacts functionality. Here, we present a computationally inexpensive methodology that combines semi-empirical dimer calculations with coarse-grained modelling to predict the packing behaviour of MOCs. This approach enables a priori prediction of the solid state and allows insight into how molecular shape and intermolecular interactions influence the crystalline phase behaviour of MOCs. Our findings provide a foundation for the rational design of MOCs with tailored solid-state structures and properties.

Heteroleptic (mixed‐ligand) coordination cages are of interest as host systems with more structurally and functionally complex cavities than homoleptic architectures. The design of heteroleptic cages, however, is far from trivial. In this work, we experimentally probed the self‐assembly of Pd(II) ions with binary ligand combinations in a combinatorial fashion to search for new cis ‐Pd ₂ L ₂ L’ ₂ heteroleptic cages. A hierarchy of computational analyses was then applied to these systems with the aim of elucidating key factors for rationalising self‐assembly outcomes. Simple and inexpensive geometric analyses were shown to be effective in identifying complementary ligand pairs. Preliminary results demonstrated the viability of relatively rapid semi‐empirical calculations for predicting the topology of thermodynamically favoured assemblies with rigid ligands, whilst more flexible systems proved challenging. Stemming from this, key challenges were identified for future work developing effective computational forecasting tools for self‐assembled metallo‐supramolecular systems.

Heteroleptic (mixed-ligand) coordination cages are of interest as host systems with more structurally and functionally complex cavities than homoleptic architectures. The design of heteroleptic cages, however, is far from trivial. In this work, we experimentally probed the self-assembly of Pd(II) ions with binary ligand combinations in a combinatorial fashion to search for new cis-Pd2L2L’2 heteroleptic cages. A hierarchy of computational analyses was then applied to these systems with the aim of elucidating key factors for rationalising self-assembly outcomes. Simple and inexpensive geometric analyses were shown to be effective in identifying complementary ligand pairs. Preliminary results demonstrated the viability of relatively rapid semi-empirical calculations for predicting the topology of thermodynamically favoured assemblies with rigid ligands, whilst more flexible systems proved challenging. Stemming from this, key challenges were identified for future work developing effective computational forecasting tools for self-assembled metallo-supramolecular systems.