Elnaz Hajizadeh

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Lecturer (Research & Teaching)

University of Melbourne

Australia

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About

Elnaz Hajizadeh is a Lecturer in Research and Teaching at The University of Melbourne, Australia. Her research areas include polymer dynamics, machine learning applications in material science, and the design of photonic metastructures, as reflected in her recent publications. She has contributed to advancements in the optimization of polymer models and the development of shape memory elastomers through innovative methodologies.

Recent Grants

Grant: Close

Materials 4.0: Artificial Intelligence-Accelerated Inverse Materials Design Engine

Open Date: 2021-04-01

Close Date: 2024-06-01

Grant: Close

Development of an ICME Paradigm for Advanced Acoustic Materials

Open Date: 2018-02-01

Close Date: 2021-05-01

Articles (13)

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2025

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2021

Year:

2021

Collaborators (5)

Peter Daivis

Professor

RMIT University

AUSTRALIA

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Mostafa Nikzad

Senior Lecturer

Swinburne University of Technology

AUSTRALIA

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Christian Brandl

Senior Lecturer

University of Melbourne

AUSTRALIA

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Ronald G. Larson

University of Michigan

UNITED STATES

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Sejeong Kim

Lecturer

University of Melbourne

AUSTRALIA

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Elnaz Hajizadeh

Research Interests

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About

Recent Grants

Articles (13)

Interpretable active learning meta-modeling for the association dynamics of telechelic polymers on colloidal particles

Linear viscoelastic shear and bulk relaxation moduli in poly(tetramethylene oxide) (PTMO) using united-atom molecular dynamics

Bayesian coarsening: rapid tuning of polymer model parameters

A combined experimental and molecular dynamic studies of curing of shape memory lignin-liquid crystalline elastomeric composites

Lignin-liquid crystalline elastomeric composites for shape memory applications and their thermomechanical properties

Asymmetric CycleGANs for inverse design of photonic metastructures

Author Correction: A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer

Three-Dimensional Printing of Shape Memory Liquid Crystalline Thermoplastic Elastomeric Composites Using Fused Filament Fabrication

A machine learning enabled hybrid optimization framework for efficient coarse-graining of a model polymer

A machine learning accelerated inverse design of underwater acoustic polyurethane coatings

Shape memory elastomers: A review of synthesis, design, advanced manufacturing, and emerging applications

Predictions of polymer migration in a dilute solution between rotating eccentric cylinders

A comparative study of force fields for predicting shape memory properties of liquid crystalline elastomers using molecular dynamic simulations

Collaborators (5)

Peter Daivis

Mostafa Nikzad

Christian Brandl

Ronald G. Larson

Sejeong Kim

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