Lan Li, Ph.D.

Bio

Lan Li is an Associate Professor of Materials Science and Engineering at Boise State University. Dr. Li finished her doctorate in Nanomaterials at the University of Cambridge in the UK, followed by working in the Bio-Nano Electronic Research Center at Toyo University in Japan. She conducted her research as a postdoc associate on the theoretical and computational studies of metal-fullerene nano-systems, hydrogen-storage materials, and metal oxide thin films at the Department of Physics, University of Florida. She then joined the Center for Materials Informatics at Kent State University in Ohio, and worked in collaboration with various national laboratories and universities on the development of computational materials research code projects and the transformation of these research codes into modules suitable for effective use in undergraduate education. Before joining Boise State, she was a NIST (National Institute of Standards and Technology)-ARRA (American Recovery and Reinvestment Act) senior fellow, working on energy and sustainability. Dr. Li has been awarded Boise State University’s Top Ten Scholar Honored Faculty, TMS (The Mineral, Metals and Materials Society) Young Leader Professional Development Award and NIST’s American Recovery and Reinvestment Act Program Fellowship. She presently serves as TMS Integrated Computational Materials Engineering Committee Programming Chair.

Education

• Postdoc Associate, Center for Materials Informatics, Kent State University, US
• Postdoc Associate, Department of Physics, University of Florida, US
• Ph.D. in Nano-materials, Department of Engineering, University of Cambridge, Cambridge, UK
• M.Phil. in Microelectronic Engineering & Semiconductor Physics, Cavendish Laboratory (Department of Physics), University of Cambridge, Cambridge, UK
• Bachelor of Science in Electronics, School of Computing, Engineering and Physical Sciences, University of Central Lancashire, Preston, UK

Areas of Interest

• Nuclear materials, electronic materials, quantum computing, and data science to accelerate materials discovery
• Funded projects: DNA-Controlled Dye Aggregation – A Path to Create Quantum Entanglement (DOE EPSCoR), Optimizing DNA-Assembled Dye Aggregates for Quantum Coherent Exciton Devices (DOD ONR), Excitonic Quantum Coherence – A Viable Path to Quantum Computing (NSF INSPIRE), Carbon Interfaces, Structure and Impacts on Reactivity (INL LDRD), In-Pile Instrumentation Initiative (DOE NEET), Hybrid Experimental-Computational Approaches to Improve and Predict Corrosion Performance of Aerospace Components (NASA EPSCoR Research Initiation Grant), Aluminum Alloy Corrosion (Boeing)

Teaching Interests

• Teaching interest: Incorporate computational modeling into undergraduate and graduate curricula 
• Courses I taught: MSE 318 Phase Transformations and Kinetics (3 credits, spring), MSE 464/564 Computational Materials Science (3 credits, spring), MSE 608 Solid State Thermodynamics (4 credits, fall)
• I have developed 19 computational modeling modules and taught the modules in 8 MSE undergraduate and graduate courses.