Dr. Li’s group utilizes first-principles density functional theory-based methods to couple with molecular dynamics, phase-field modeling, data science, AI/machine learning and experiments. The research spans from materials growth to property screening, from device simulation to materials informatics. Dr. Li’s group widely collaborates with national labs, industry and other universities, including Naval Research Laboratory (NRL), National Institute of Standards and Technology (NIST), Idaho National Lab (INL), Center for Advanced Energy Studies (CAES), Boeing, Brewer Science Inc. Dr. Li has received funding from DOE, ONR, NSF, NASA and NIST.
DNA-Templated Dye Aggregates for Quantum Entanglement and Quantum Information Applications
Various dyes are computationally screened using a combination of ab-initio, molecular dynamics and machine learning methods. We predict their ground state and excited state properties, reveal dye structure feature-property relationships to fundamentally understand quantum entanglement, and explore hydrophobicity and polarity effects on dyes for quantum coherent exciton devices.
Nuclear Materials and Sensor Devices
Ab-initio and molecular dynamics modeling techniques are applied to comprehensively study corrosion mechanism on UN surfaces (as shown in the figure below (left)). We derive design methods of stopping & preventing UN corrosion, and guide oxidation and mechanical testing of fuel materials conducted at BSU and INL.
To design high temperature irradiation resistant sensors for nuclear reactors, we combine ab-initio, molecular dynamics modeling, and Boltzmann Transport theory to predict the performance of various metals and alloys under extreme temperatures (as shown in the figure below (right)). Grain growth and fracture mechanism of sensor materials after heat treatment are also investigated with phase field modeling.
Low-Dimensional Materials Growth, Properties and Performance
We harness the power of low-dimensional materials data and advanced computational modeling techniques to reveal processing-structure-property-performance relationships. The projects include:
- Machine learning-assisted study of precursors and their interactions with substrates to guide materials synthesis
- Combined machine learning and thermodynamic calculations to design heterostructured materials for electronic applications
Carbon Capture and Storage
Porous materials are commonly used to capture and store carbon dioxide from air. Besides computational modeling, we leverage data science and machine learning to accelerate the multiscale modeling workflows for porous materials design.