Machine learning in combinatorial polymer chemistry
- 5 February 2021
- journal article
- editorial
- Published by Springer Science and Business Media LLC in Nature Reviews Materials
- Vol. 6 (8), 642-644
- https://doi.org/10.1038/s41578-021-00282-3
Abstract
The design of new functional polymers depends on the successful navigation of their structure-function landscapes. Advances in combinatorial polymer chemistry and machine learning provide exciting opportunities for the engineering of fit-for-purpose polymeric materials. The design of new functional polymers depends on the successful navigation of their structure-function landscapes. Advances in combinatorial polymer chemistry and machine learning provide exciting opportunities for the engineering of fit-for-purpose polymeric materials.Keywords
This publication has 10 references indexed in Scilit:
- Targeted sequence design within the coarse-grained polymer genomeScience Advances, 2020
- Frequency-dependent dielectric constant prediction of polymers using machine learningnpj Computational Materials, 2020
- Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular SimulationThe Journal of Physical Chemistry B, 2020
- Automation of Controlled/Living Radical PolymerizationAdvanced Intelligent Systems, 2019
- BigSMILES: A Structurally-Based Line Notation for Describing MacromoleculesACS Central Science, 2019
- Up in the air: oxygen tolerance in controlled/living radical polymerisationChemical Society Reviews, 2018
- Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approachNature Materials, 2016
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovationAPL Materials, 2013
- Semi‐Automated Synthesis and Screening of a Large Library of Degradable Cationic Polymers for Gene DeliveryAngewandte Chemie-International Edition, 2003
- Combinatorial Methods, Automated Synthesis and High‐Throughput Screening in Polymer Research: Past and PresentMacromolecular Rapid Communications, 2003