Design of a homologous series of molecular glassformers

Overcoming the barrier: designing novel thermally robust shape memory vitrimers by establishing a new machine learning framework

Shape memory vitrimers (SMVs) are an emerging class of advanced materials that have garnered significant interest from researchers in the past five to six years. These materials can return to their original shape when exposed to a stimulus, while also healing damage they have sustained. However, achieving both high healing/recycling efficiency and a high glass transition temperature (Tg) in SMVs has been challenging, due to the conflicting requirements between molecular chain mobility and the formation and reaction of dynamic covalent bond exchange. Based on the understanding of chemo-physical properties, this study first leverages machine learning (ML), involving supervised and unsupervised learning approaches, to navigate this complex design space of SMVs. Furthermore, we elaborated the basic mathematical frameworks of ML approaches and comprehensively compared their performances. Based on the best performing model, we designed four types of thermally robust shape memory vitrimers (TRSMVs), which boast high recycling efficiency, elevated Tg, and exemplary shape memory effects, overcoming conventional barriers. One of the discovered samples exhibited outstanding performance with a Tg of 233.5 °C, a recycling efficiency of 84.1%, and a recovery stress of 33 MPa in experiments. It aligns well with ML predictions, showcasing the potential of our ML framework in driving innovative materials design and advancing the field of smart polymers.

Dissymmetric Triaryltriazines: Small Mass Columnar Glasses

Columnar liquid crystals with very small molecular masses that form anisotropic glasses well above room temperature are obtained by mixed dissymmetric substitution of sym-triazine with ester-bearing phenyl and phenanthryl or tetrahelicenyl moieties. The combination of low molecular symmetry with configurational flexibility and short polar ester moieties stabilizes the mesophase over large temperature ranges and induces pronounced calorimetric glass transitions within the anisotropic fluid despite the smallness of the molecules. In contrast to more symmetrical homologs, no ester tails longer than ethyl are necessary to induce the liquid crystalline state, allowing for the near-absence of any insulating and weight-increasing alkyl periphery. Films drop-cast from solution show in all cases emission spectra that do not show significant change of fluorescence emission upon annealing, indicating that the columnar hexagonal mesoscopic order is obtained directly upon deposition from solution and is resistant to crystallization upon annealing.

An algorithm to classify homologous series within compound datasets

Homologous series are groups of related compounds that share the same core structure attached to a motif that repeats to different degrees. Compounds forming homologous series are of interest in multiple domains, including natural products, environmental chemistry, and drug design. However, many homologous compounds remain unannotated as such in compound datasets, which poses obstacles to understanding chemical diversity and their analytical identification via database matching. To overcome these challenges, an algorithm to detect homologous series within compound datasets was developed and implemented using the RDKit. The algorithm takes a list of molecules as SMILES strings and a monomer (i.e., repeating unit) encoded as SMARTS as its main inputs. In an iterative process, substructure matching of repeating units, molecule fragmentation, and core detection lead to homologous series classification through grouping of identical cores. Three open compound datasets from environmental chemistry (NORMAN Suspect List Exchange, NORMAN-SLE), exposomics (PubChemLite for Exposomics), and natural products (the COlleCtion of Open NatUral producTs, COCONUT) were subject to homologous series classification using the algorithm. Over 2000, 12,000, and 5000 series with CH2 repeating units were classified in the NORMAN-SLE, PubChemLite, and COCONUT respectively. Validation of classified series was performed using published homologous series and structure categories, including a comparison with a similar existing method for categorising PFAS compounds. The OngLai algorithm and its implementation for classifying homologues are openly available at: https://github.com/adelenelai/onglai-classify-homologues .

The role of intramolecular relaxations on the structure and stability of vapor-deposited glasses

Stable glasses (SGs) are formed through surface-mediated equilibration (SME) during physical vapor deposition (PVD). Unlike intermolecular interactions, the role of intramolecular degrees of freedom in this process remains unexplored. Here, using experiments and coarse-grained molecular dynamics simulations, we demonstrate that varying dihedral rotation barriers of even a single bond, in otherwise isomeric molecules, can strongly influence the structure and stability of PVD glasses. These effects arise from variations in the degree of surface mobility, mobility gradients, and mobility anisotropy, at a given deposition temperature ( T dep). At high T dep, flexible molecules have access to more configurations, which enhances the rate of SME, forming isotropic SGs. At low T dep, stability is achieved by out of equilibrium aging of the surface layer. Here, the poor packing of rigid molecules enhances the rate of surface-mediated aging, producing stable glasses with layered structures in a broad range of T dep. In contrast, the dynamics of flexible molecules couple more efficiently to the glass layers underneath, resulting in reduced mobility and weaker mobility gradients, producing unstable glasses. Independent of stability, the flattened shape of flexible molecules can also promote in-plane orientational order at low T dep. These results indicate that small changes in intramolecular relaxation barriers can be used as an approach to independently tune the structure and mobility profiles of the surface layer and, thus, the stability and structure of PVD glasses.

Role of Molecular Layering in the Enhanced Mechanical Properties of Stable Glasses

The density, degree of molecular orientation, and molecular layering of vapor-deposited stable glasses (SGs) vary with substrate temperature (T_dep) below the glass-transition temperature (T_g). Density and orientation have been suggested to be factors influencing the mechanical properties of SGs. We perform nanoindentation on two molecules which differ by only a single substituent, allowing one molecule to adopt an in-plane orientation at low T_dep. The reduced elastic modulus and hardness of both molecules show similar T_dep dependences, with enhancements of 15-20% in reduced modulus and 30-45% in hardness at T_dep ≈ 0.8T_g, where the density of vapor-deposited films is enhanced by ∼1.4% compared to that of the liquid-quenched glass. At T_dep < 0.8T_g, one of the molecules produces highly unstable glasses with in-plane orientation. However, both systems show enhanced mechanics. Both the modulus and hardness correlate with the degree of layering, which is similar in both systems despite their variable stability. We suggest that nanoindentation performed normal to the film's surface is influenced by the tighter packing of the molecules in this direction.