A hybrid molecular dynamics–machine learning framework for boiling point estimation in aromatic fluids

A hybrid molecular dynamics–machine learning framework for boiling point estimation in aromatic fluids

Precise estimation of boiling points in organic fluids is critical for designing efficient and safe thermal systems. This study presents a hybrid molecular dynamic (MD)–machine learning (ML) framework for boiling point estimation in two representative aromatic fluids: biphenyl (C12H10) and diphenyl...

Full description

Saved in:
Bibliographic Details
Main Authors: Amirali Shateri, Zhiyin Yang, Nasser Sherkat, Jianfei Xie
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Case Studies in Thermal Engineering
Subjects:
Machine learning
Molecular dynamics simulation
Boiling point
Thermal fluids
Phase change
Online Access:http://www.sciencedirect.com/science/article/pii/S2214157X2500944X
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items