Work

Data-driven machine learning optimisation of silver nanoparticle synthesis

Year: 2026

Type: article

Abstract: Surrogate-based inverse design framework that identifies the synthesis conditions required to reach a user-defined target Ag-NP size.

Source: Nanoscale Advances

DOI: 10.1039/d6na00557h

Authors: Joseph Carver, Y. M. John Chew, Semali Perera, Sourav Chatterjee

Institution: University of Bath

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,962

Funder: University of Bath

Award: G2690106896

OpenAlex ID: W7213873846

FWCI:

Cites: 44

Cited by:

Related to: