Portrait of

Houlong Zhuang

Assistant Professor, School for Engineering of Matter, Transport and Energy

Total mentored projects: 10

Dr. Houlong Zhuang  is an assistant professor in the School for Engineering of Matter, Transport and Energy at ASU. He received his doctorate in materials science and engineering at Cornell University. Prior to ASU, Dr. Zhuang was a postdoctoral researcher at Princeton University. Dr. Zhuang’s current research interests are quantum mechanical simulations, machine learning, and quantum computing. 

ASU Search

Spring 2023

Kevin Coutinho

Mechanical engineering

Thermoelectric Material Discovery Using Machine Learning

Using data-driven machine learning models and density functional theory will help identify potential new thermoelectric materials.

Program: MORE

Parin Trivedi

Aerospace engineering

Hydrogen Embrittlement Prediction Using Machine Learning

Using machine learning to predict hydrogen embrittlement will improve hydrogen storage so the world can shift to a sustainable alternative fuel.

Program: FURI

Fall 2020

Sreeharsha Lakamsani

Computer science

Neural ODE Modeling for Many-Body Interactions

Using neural ordinary differential equations to predict stochastic particle behavior will allow for prediction of the start and end behavior of cancer cells and tumors.

Program: FURI

Eugene Garay Agravante

Mechanical engineering

Utilizing Density Functional Theory to Compute the Efficacy of a Rippled Janus Structure for use in Optoelectronics

Testing how certain sections of an atomic structure bind differently will lead to more efficient structures for use in advanced electronics.

Program: FURI

Spring 2020

Sreeharsha Lakamsani

Computer science

Neural ODE Modeling for Many-Body Interactions

Developing a neural network model for predictive modeling of many-body interactions will help simulate collective dynamics of cancer cells.

Program: FURI

Eugene Garay Agravante

Mechanical engineering

Utilizing Density Functional Theory to Compute the Efficacy of a Practical Material for Industrial Direct Air Capture via a Copper Metal Organic Framework

Computational modeling of a specific atomic structure that is efficient at capturing CO2 from the air can help fight climate change.

Program: FURI

Spring 2019

Chance Johnathan Price

Aerospace engineering

Discovering Tesselations of 2D Piezomagnetic Materials

Studying patterns in 2D materials can lead to discovering new useful material properties for technologies such as batteries.

Program: FURI

Wenjiang Huang

Civil, environmental and sustainable engineering

ML Phase Prediction of High-Entropy Alloys

Using machine learning to explore phase selection rules will help discover novel metal alloys with useful properties.

Program: MORE

Fall 2018

Pedro Martin

Mechanical engineering

Machine Learning Models for Accelerating Discovery and Design of Energy-Related Materials

Program: FURI

Immanuella Kankam

Mechanical engineering

Ab Initio Playing Pentagon Puzzles

Program: MORE

Needs Review

Kevin Coutinho

Mechanical engineering

Thermoelectric Material Discovery Using Machine Learning

Using data-driven machine learning models and density functional theory will help identify potential new thermoelectric materials.

Program: MORE

Parin Trivedi

Aerospace engineering

Hydrogen Embrittlement Prediction Using Machine Learning

Using machine learning to predict hydrogen embrittlement will improve hydrogen storage so the world can shift to a sustainable alternative fuel.

Program: FURI