Systematic exploration of accessible topologies of cage molecules <i>via</i> minimalistic models

Tarzia, Andrew; Wolpert, Emma H.; Jelfs, Kim E.; Pavan, Giovanni M.

doi:10.1039/d3sc03991a

Cited by 3 publications

(3 citation statements)

References 69 publications

(125 reference statements)

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“…Despite the structural stability present herein, we must pay attention to different molecular design rules that may significantly affect the stability of their final structures, such as a molecular design strategy proposed recently to explore the energy‐stable molecular topology by tuning bond angles of building blocks and assembling the building blocks into different types of topological cages. [ 21 ] Theoretically, it is true that some configurations are more unstable than others and occupy higher energy state in their potential energy landscape (PEL). [ 26,31 ] From the PEL viewpoint, the structural stability of our melt‐quenched structures is likely originated from the fact that, by tuning forcefield parameters, the topography of PEL varies accordingly but our melt‐quenching rule enables each structure to fully relax in its own PEL with no confinement, so as to reach a deep local minimum.…”

Section: Resultsmentioning

confidence: 99%

“…[ 18–20 ] Impressively, recent study inspires us with automatic design of topology‐accessible molecular assembly by devising its constitutive building blocks. [ 21 ] However, built upon spatial arrangement of particle‐level blocks, such particle systems such as glasses are traditionally stereotyped as the pristine bulk elements of architected materials—rather than the architected material itself. [ 22,23 ] rendering it elusive whether mechanical metamaterials can be feasible at atomistic or microparticle level, [ 6,24 ] let alone the atomistic lesson of structural disorder in devising metamaterials.…”

Section: Introductionmentioning

confidence: 99%

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De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

Liu,

Li,

Wei

et al. 2024

Advanced Science

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Architected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so‐termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse‐resolution, ordered‐pattern design space. Here, combining high‐throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some intriguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight‐yet‐stiff cellular materials featuring a theoretical limit of linear stiffness–density scaling, whose structural disorder—rather than order—is key to reduce the scaling exponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in‐between directional and non‐directional bonding such as covalent and ionic bonds, modest bond directionality is most likely to promotes disordered packing of polyhedral, stretching‐dominated structures responsible for the formation of metamaterials. This work pioneers a bottom‐down atomistic scheme to design mechanical metamaterials formatted disorderly, unlocking a largely untapped field in leveraging structural disorder in devising metamaterials atomistically and, potentially, generic to conventional upscaled designs.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning

Liu,

Li,

Wei

et al. 2024

Advanced Science

View full text Add to dashboard Cite

show abstract

“…Looking ahead, one can foresee major developments coming from molecular modelling approaches where simple tools can enable screening in silico the formation of cage-type structures which will be particularly informative for flexible systems. 158 Combining such computational tools with robotic screening will significantly speed up the discovery process and expand the range of complex structures that can be produced. 159 With the escalating impact of artificial intelligence, 160,161 this growing body of data will improve the predictability of complex synthesis of discrete assemblies.…”

Section: Discussionmentioning

confidence: 99%