Work

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

Year: 2022

Type: article

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

Source: Molecular Systems Design & Engineering

DOI: 10.1039/d1me00187f

Authors: Siva Dasetty, Igor Coropceanu, Joshua Portner, Jiyuan Li, Juan J. de Pablo, Dmitri Talapin, Andrew L. Ferguson

Institution: University of Chicago

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, U.S. Department of Energy, Midwest Integrated Center for Computational Materials, Office of Science, Division of Materials Research, Basic Energy Sciences

Awards: G2879087551, MRI: Acquisition of a high-performance GPU-based computer for advanced multiscale materials modeling, G7820774471

OpenAlex ID: W4206931478

FWCI: 0.352

Cites: 63

Cited by: 6

Related to: 10