Advancing material property prediction: using physics-informed machine learning models for viscosity

Advancing material property prediction: using physics-informed machine learning models for viscosity

Abstract In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science do...

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Bibliographic Details
Main Authors: Alex K. Chew, Matthew Sender, Zachary Kaplan, Anand Chandrasekaran, Jackson Chief Elk, Andrea R. Browning, H. Shaun Kwak, Mathew D. Halls, Mohammad Atif Faiz Afzal
Format: Article
Language:English
Published: BMC 2024-03-01
Series:Journal of Cheminformatics
Subjects:
Classical molecular dynamics simulations
Organic molecules
Physical properties
Viscosity
Quantitative structure–property relationships
Machine learning
Online Access:https://doi.org/10.1186/s13321-024-00820-5

Internet

https://doi.org/10.1186/s13321-024-00820-5

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