Scientific discovery in the age of artificial intelligence
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …
and accelerate research, helping scientists to generate hypotheses, design experiments …
[HTML][HTML] Photocontrolled RAFT polymerization: past, present, and future
In this review, we provide a brief history, progress, and applications, and discuss the
remaining challenges of photocontrolled reversible addition–fragmentation chain transfer …
remaining challenges of photocontrolled reversible addition–fragmentation chain transfer …
[HTML][HTML] Applied machine learning as a driver for polymeric biomaterials design
SM McDonald, EK Augustine, Q Lanners… - Nature …, 2023 - nature.com
Polymers are ubiquitous to almost every aspect of modern society and their use in medical
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …
traditional research paradigms in the era of artificial intelligence and automation. An …
[HTML][HTML] Open-air green-light-driven ATRP enabled by dual photoredox/copper catalysis
Photoinduced atom transfer radical polymerization (photo-ATRP) has risen to the forefront of
modern polymer chemistry as a powerful tool giving access to well-defined materials with …
modern polymer chemistry as a powerful tool giving access to well-defined materials with …
Machine learning on a robotic platform for the design of polymer–protein hybrids
Polymer–protein hybrids are intriguing materials that can bolster protein stability in non‐
native environments, thereby enhancing their utility in diverse medicinal, commercial, and …
native environments, thereby enhancing their utility in diverse medicinal, commercial, and …
Benchmarking machine learning models for polymer informatics: an example of glass transition temperature
In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the
glass transition temperature T g and other properties of polymers has attracted extensive …
glass transition temperature T g and other properties of polymers has attracted extensive …
Emerging trends in machine learning: a polymer perspective
In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …
intelligence as applied to polymer science. Here, we highlight the unique challenges …
[HTML][HTML] Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites
This comprehensive review discusses the recent progress in synthesis, properties,
applications, 3D printing and machine learning of graphene quantum dots (GQDs) in …
applications, 3D printing and machine learning of graphene quantum dots (GQDs) in …
Chemically specific coarse‐graining of polymers: Methods and prospects
S Dhamankar, MA Webb - Journal of Polymer Science, 2021 - Wiley Online Library
Coarse‐grained (CG) modeling is an invaluable tool for the study of polymers and other soft
matter systems due to the span of spatiotemporal scales that typify their physics and …
matter systems due to the span of spatiotemporal scales that typify their physics and …