Abstract Machine learning holds significant research potential in the field of nanotechnology,
enabling nanomaterial structure and property predictions, facilitating materials design and …

Polymer processing, purification, and self-assembly have significant roles in the design of
polymeric materials. Understanding how polymers behave in solution (eg, their solubility …

The development of new polymer materials is an emerging field for more than 100 years.
However, it is currently facing major challenges and the application of digital methods can …

A simple approach was developed to computationally construct a polymer dataset by
combining simplified molecular-input line-entry system (SMILES) strings of a targeted …

For using machine learning to predict material properties, the feature representation of the
materials given to the model plays a fundamental role. A model describes material …

Over the past decade, it has become abundantly clear that there is no esca** the
emerging growth of artificial intelligence (AI) in every facet of our life. The advanced …

The partitioning of ions between polymeric ion-exchange membranes (IEMs) and the
surrounding liquid is governed by the activity coefficients of the ions, which, in turn …

Block copolymers, composed of poly (2-oxazoline) s and poly (2-oxazine) s, can serve as
drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent …

Abstract Machine learning is increasingly being applied in polymer chemistry to link
chemical structures to macroscopic properties of polymers and to identify chemical patterns …

Abstract Machine learning has rapidly advanced the design and discovery of new materials
with targeted applications in various systems. First-principles calculations and other …