Unsupervised learning of sequence-specific aggregation behavior for a model copolymer-Reference-Cited by-同舟云学术

Unsupervised learning of sequence-specific aggregation behavior for a model copolymer

Published:2021 Issue:33 Volume:17 Page:7697-7707
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Statt Antonia¹,Kleeblatt Devon C.²,Reinhart Wesley F.²³^ORCID

Affiliation:

1. Materials Science and Engineering, Grainger College of Engineering, University of Illinois, Urbana-Champaign, IL 61801, USA

2. Materials Science and Engineering, Pennsylvania State University, University Park, PA 16802, USA

3. Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA

Abstract

Unsupervised machine learning is applied to study the disordered aggregates of a model sequence defined macromolecule. Using these learned collective variables provides new insight into both the structure and kinetics of these aggregates.

Funder

Materials Research Institute, Pennsylvania State University

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2021/SM/D1SM01012C

Reference50 articles.

1. Targeted sequence design within the coarse-grained polymer genome

2. Bias free multiobjective active learning for materials design and discovery

3. Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

4. Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

5. Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics

Cited by 35 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A review of computational approaches used in the modelling, design, and manufacturing of biodegradable and biobased polymers;Progress in Polymer Science;2024-09

2. A prospective on machine learning challenges, progress, and potential in polymer science;MRS Communications;2024-07-01

3. Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning;Biophysical Journal;2024-07

4. Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning;2024-03-30

5. Machine Learning in Soft Matter: From Simulations to Experiments;Advanced Functional Materials;2024-01-31