Cross Reference Logo
Citations to this article as recorded by CrossRef and RSC Journals (15 citations).

Sequence-defined structural transitions by calcium-responsive proteins
Marina P. Chang, Winnie Huang, Gatha M. Shambharkar, Kenny M. Hernandez and Danielle J. Mai
Polym. Chem., 2024, 15, 4864
DOI: 10.1039/D4PY00907J

Predicting self-assembly of sequence-controlled copolymers with stochastic sequence variation
Kaleigh A. Curtis, Antonia Statt and Wesley F. Reinhart
Soft Matter, 2025, 21, 2143
DOI: 10.1039/D4SM01219D

Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations
Nickolas Gantzler, Aryan Deshwal, Janardhan Rao Doppa and Cory M. Simon
Digital Discovery, 2023, 2, 1937
DOI: 10.1039/D3DD00117B

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows
Erin C. Day, Supraja S. Chittari, Matthew P. Bogen and Abigail S. Knight
ACS Polym. Au, 2023, 3, 406
DOI: 10.1021/acspolymersau.3c00025

A review on electrolyte innovation enabled by machine learning for energy storage applications
Nishant Shukla, Manashi Saikia and Madhuryya Deka
Materials Today Physics, 2025, 57, 101799
DOI: 10.1016/j.mtphys.2025.101799

Predicting heteropolymer phase separation using two-chain contact maps
Jessica Jin, Wesley Oliver, Michael A. Webb and William M. Jacobs
The Journal of Chemical Physics, 2025, 163
DOI: 10.1063/5.0269504

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation
Varad Daoo and Jayant K. Singh
adv. intell. discov., 2025
DOI: 10.1002/aidi.202500109

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers
Agrim Babbar, Sriram Ragunathan, Debirupa Mitra, Arnab Dutta and Tarak K. Patra
Journal of Polymer Science, 2024, 62, 1175
DOI: 10.1002/pol.20230714

Accelerating In Silico Discovery of Metal–Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning
Varad Daoo and Jayant K. Singh
ACS Appl. Mater. Interfaces, 2024, 16, 6971
DOI: 10.1021/acsami.3c14505

Thermogelation of methylcellulose: A rheological approach with Gaussian Process Regression
Marie Sourroubille, Isaac Y. Miranda-Valdez, Tero Mäkinen, Juha Koivisto and Mikko J. Alava
Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2025, 709, 136057
DOI: 10.1016/j.colsurfa.2024.136057

Machine learning in polymer science: A new lens for physical and chemical exploration
Xiaoqin Cao, Yongqing Zhang, Zhenghua Sun, Hongyao Yin and Yujun Feng
Progress in Materials Science, 2026, 156, 101544
DOI: 10.1016/j.pmatsci.2025.101544

Mechanical property prediction of random copolymers using uncertainty-based active learning
Wei-Che Chang, Zong-Yun Tsai, Chin-Wen Chen, Chi-Hua Yu and Chuin-Shan Chen
Computational Materials Science, 2025, 247, 113489
DOI: 10.1016/j.commatsci.2024.113489

Predicting possible polymer sequences based on given adhesive free energies of polymer-surface interactions using deep learning
Liyang Xu, Yi Yang and Bo Yin
Chemical Physics Letters, 2025, 878, 142152
DOI: 10.1016/j.cplett.2025.142152

The current science of sequence-defined macromolecules
Karen Hakobyan, Benjamin B. Noble and Jiangtao Xu
Progress in Polymer Science, 2023, 147, 101754
DOI: 10.1016/j.progpolymsci.2023.101754

Analysis of Macromolecular Systems as Enabler for Energy and Life Science Applications
Ilya Anufriev, Jonas Debbeler, Anja Traeger and Ivo Nischang
Macro Chemistry & Physics, 2024, 225
DOI: 10.1002/macp.202400055