Hong Research Group

Materials Design and Discovery via Quantum Mechanics and Machine Learning

MAterials Properties Prediction (MAPP)

built on experimental data
empowered by artificial intelligence
enhanced by ab initio calculations

About Us

Our group is in the School for Engineering of Transport, Energy and Matter at the Arizona State University. The principle investigator, Dr. Qijun Hong, runs an active research team which consists of graduate students and undergraduate students.

Research

We use quantum mechanics and machine learning for materials design and discovery.

News

2022.8.13 Paper “Melting temperature prediction via first principles and deep learning” is published in Computational Materials Science.

2022.6.1 We are grateful that NSF supports our research of materials under extreme conditions.

2022.4.25 We deploy online here a machine learning model that predicts the bulk modulus of materials.

2022.4.8 Welcome to new ME graduate student Sida Xue to our Group!

2022.3.26 Welcome to new MSE graduate student Audrey Campbell to our Group!

2022.3.22 We deploy online here a machine learning model that predicts the critical temperature of superconductors.

2022.1.25 Paper “Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US” is published in PNAS. Visit my covid model here, one of the best models in forecasting US national fatality.

2021.11.16 Qijun is nominated as one of the finalists for the Rising Stars in Computational Materials Science, organized by Elsevier.

2021.9.26 We deploy online here a machine learning model that predicts melting temperature.