Machine Learning-Based Recommender System for Pulsed Laser Ablation in Liquid: Recommendation of Optimal Processing Parameters for Targeted Nanoparticle Size and Concentration Using Cosine Similarity and KNN Models

Machine Learning-Based Recommender System for Pulsed Laser Ablation in Liquid: Recommendation of Optimal Processing Parameters for Targeted Nanoparticle Size and Concentration Using Cosine Similarity and KNN Models

Achieving targeted nanoparticle (NP) size and concentration combinations in Pulsed Laser Ablation in Liquid (PLAL) remains a challenge due to the highly nonlinear relationships between laser processing parameters and NP properties. Despite the promise of PLAL as a surfactant-free, scalable synthesis...

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Bibliographic Details
Main Authors: Anesu Nyabadza, Dermot Brabazon
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Crystals
Subjects:
machine learning
recommender systems
XGBoost
support vector regressor
automated nanoparticle fabrication
pulsed laser ablation in liquid
Online Access:https://www.mdpi.com/2073-4352/15/7/662
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