Bryant Y. Li

Bryant Y. Li

Ph.D. Candidate in Materials Science | Researching Solid-State Batteries and Machine Learning Applications | Materials informatics Expert

Talks and presentations

Machine Learning Aided Atomistic Modeling of Solid State Electrolyte Interphase Formation for Li/Li₇P₃S₁₁

June 01, 2025

Oral Presentation, AI4X 2025 International Conference, Singapore

Invited oral presentation on the application of machine learning interatomic potentials to atomistically model solid-electrolyte interphase (SEI) formation in sulfide-based solid-state batteries. Presented the end-to-end workflow from DFT data generation to MLIP training, large-scale MD simulation, and structural analysis of interfacial decomposition products.

An Atomistic Study of Reactivity in Solid State Electrolyte Interphase Formation for Li/Li₇P₃S₁₁ Systems

April 01, 2025

Oral Presentation, Materials Research Society (MRS) Spring Meeting, Seattle, WA, USA

Oral presentation on atomistic molecular dynamics simulations of solid-electrolyte interphase (SEI) formation at the Li metal/Li₇P₃S₁₁ interface using machine-learned interatomic potentials. Results reveal key decomposition pathways and reaction mechanisms governing sulfide solid-state battery interfaces.

Awaruite and Tetrataenite Driven NH₃ Synthesis

December 14, 2015

Poster Presentation, American Geophysical Union (AGU) Fall Meeting, San Francisco, CA, USA

Presented findings on catalytic ammonia production on Fe–Ni alloy clusters (Awaruite and Tetrataenite) as a mechanism relevant to the origins of life in the Archean Era. Classical potentials (Finnis–Sinclair, Lennard–Jones, Sutton–Chen) were used to model cluster thermodynamic stability and predict adsorption behavior of nitrogen and hydrogen species.