scikit-matter : A Suite of Generalisable Machine Learning Methods Born out of Chemistry and Materials Science.

Easy-to-use libraries such as scikit-learn have accelerated the adoption and application of machine learning (ML) workflows and data-driven methods. While many of the algorithms implemented in these libraries originated in specific scientific fields, they have gained in popularity in part because of their generalisability across multiple domains. Over the past two decades, researchers in the chemical and materials science community have put forward general-purpose machine learning methods.

Ranking the synthesizability of hypothetical zeolites with the sorting hat.

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors.

A new kind of atlas of zeolite building blocks.

We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether a more general representation of the atomic structure, furnished by the smooth overlap of atomic position (SOAP) method, is required to capture accurately structure-property relations. We assessed the quality of each descriptor by machine-learning the molar energy and volume for each hypothetical framework in the dataset. We have found that a SOAP representation with a cutoff length of 6 Å, which goes beyond near-neighbor tetrahedra, best describes the structural diversity in the Deem database by capturing relevant interatomic correlations.

Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank.

Rationalizing the structure and structure-property relations for complex materials such as polymers or biomolecules relies heavily on the identification of local atomic motifs, e.g., hydrogen bonds and secondary structure patterns, that are seen as building blocks of more complex supramolecular and mesoscopic structures.

Fracture-Healing Kinetics of Thermoreversible Physical Gels Quantified by Shear Rheophysical Experiments.

The fracture-healing behavior of model physically associating triblock copolymer gels was investigated with experiments coupling shear rheometry and particle tracking flow visualization. Fractured gels were allowed to rest for specific time durations, and the extent of strength recovered during the resting time was quantified as a function of temperature (20-28 °C) and gel concentration (5-6 vol %). Measured times for full strength recovery were an order of magnitude greater than characteristic relaxation times of the system.