Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices-Reference-Cited by-同舟云学术

Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices

Published:2022 Issue:4 Volume:7 Page:350-363
ISSN:2058-9689
Container-title:Molecular Systems Design & Engineering
language:en
Short-container-title:Mol. Syst. Des. Eng.

Author:

Dasetty Siva¹,Coropceanu Igor²^ORCID,Portner Joshua²,Li Jiyuan¹,de Pablo Juan J.¹^ORCID,Talapin Dmitri¹²³,Ferguson Andrew L.¹^ORCID

Affiliation:

1. Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA

2. Department of Chemistry, University of Chicago, Chicago, Illinois 60637, USA

3. James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA

Abstract

Coarse-grained molecular modeling and active learning enable the design of polarizable nanoparticles capable of triggered assembly and disassembly.

Funder

Division of Materials Research

Basic Energy Sciences

Publisher

Royal Society of Chemistry (RSC)

Subject

Materials Chemistry,Industrial and Manufacturing Engineering,Process Chemistry and Technology,Energy Engineering and Power Technology,Biomedical Engineering,Chemical Engineering (miscellaneous),Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/ME/D1ME00187F

Reference71 articles.

1. From predictive modelling to machine learning and reverse engineering of colloidal self-assembly

2. Functional materials and devices by self-assembly

3. Self-Assembly of Colloidal Nanocrystals: From Intricate Structures to Functional Materials

4. Inverse methods for design of soft materials

5. Anisotropy of building blocks and their assembly into complex structures

Cited by 3 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. Data-Driven Discovery of Linear Molecular Probes with Optimal Selective Affinity for PFAS in Water;Journal of Chemical & Engineering Data;2023-11-02

2. Application of machine-learning algorithms to predict the transport properties of Mie fluids;The Journal of Chemical Physics;2023-07-12

3. Polymer sequence design via molecular simulation-based active learning;Soft Matter;2023