Hong Research Group
Materials Design and Discovery via Quantum Mechanics and Deep Learning
built on experimental data
empowered by artificial intelligence
enhanced by ab initio calculations
We use quantum mechanics and deep learning for materials design and discovery.
2024.2.16 Qijun is awarded the CALPHAD Young Leader Award from the CALPHAD Advisory and Editorial Boards.
2024.1.16 Paper “Interfacial degradation of the NMC/Li6PS5Cl composite cathode in all-solid-state batteries” is published in Journal of Materials Chemistry A.
2023.12.24 Paper “Computationally Led High Pressure Synthesis and Experimental Thermodynamics of Rock Salt Yttrium Monoxide” is published in Chemistry of Materials.
2023.9.29 Qijun is part of the winning team (ASU + UT Austin + NREL) of the DOE Energy Earthshots Initiative. See DoE Press Release.
2023.4.17 Welcome to research scientist Ligen Wang to our Group!
2023.3.21 Qijun is part of the winning team (UVA+ASU) of the DoD MURI Program in 2023. See DoD Press Release.
2022.12.2 Paper “Integrating computational and experimental thermodynamics of refractory materials at high temperature” is published in Calphad.
2022.9.17 Paper “Melting temperature prediction using a graph neural network model: from ancient minerals to new materials” is featured on the ASU Fulton Schools in the News, Phys.org, along with other news outlets.
2022.8.31 Paper “Melting temperature prediction using a graph neural network model: from ancient minerals to new materials” is published in PNAS.
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.