Optimal design of polymers is a challenging task due to their enormous chemical and
configurational space. Recent advances in computations, machine learning, and increasing …

Polymers are incredibly versatile materials and have become ubiquitous. Increasingly,
researchers are using data science and polymer informatics to design new materials and …

Continuous development of the mining industry has led to the production of large volumes of
solid wastes. Toxic elements (TEs) have been identified in mining-associated solid wastes …

We apply a recently developed unsupervised machine learning scheme for local
environments [Reinhart, Comput. Mater. Sci., 2021, 196, 110511] to characterize large …

Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in
which the local arrangement of chemical moieties can lead to the formation of long-range …

Polymers, with the capacity to tunably alter properties and response based on manipulation
of their chemical characteristics, are attractive components in biomaterials. Nevertheless …

Single-chain nanoparticles (SCNPs) are intriguing materials inspired by proteins that consist
of a single precursor polymer chain that has collapsed into a stable structure. In many …

Accurate and automatic segmentation of pancreatic tumors and organs from medical images
is important for clinical diagnoses and making treatment plans for patients with pancreatic …

Unique structure representation of polymers plays a crucial role in developing models for
polymer property prediction and polymer design by data-centric approaches. Currently …

Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are
linked to their compositions. However, it is challenging to establish a universal composition …