README.md

metadata

title: LiB Simulation AI Engine
emoji: 🔋
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
short_description: ReaxFF reparameterization & LiF SEI analytics dashboard

🔋 Lithium-Ion Battery Simulation AI Engine

A comprehensive analytics dashboard for ReaxFF force-field reparameterization, LiF solid-electrolyte interphase (SEI) modelling, and Li⁺ transport prediction in lithium-ion batteries.

Features

• Force Field Performance — R² / RMSE comparison across Yun et al., Wang et al., and new reparameterization
• Li Diffusion & Arrhenius — Interactive diffusion coefficient plots, Arrhenius analysis at 300/400/500 K
• Crystal Stability — Energy-strain curves and Murnaghan equation of state for LiF
• RDF Evolution — Radial distribution function time series showing crystalline-to-amorphous transition
• SEI Component Analysis — Multi-criteria ranking of LiF, Li₂CO₃, Li₂O, and other SEI components
• ML Force Field Comparison — Benchmarking of M3GNet, CHGNet, NequIP, DeepMD, SchNet, ALIGNN-FF
• Simulation Campaign — Full database and CI-NEB energy barrier summary

Data Sources

All quantitative data comes directly from:

De Angelis P., Cappabianca R., Fasano M., Asinari P., Chiavazzo E. Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol. Scientific Reports 14:978 (2024). DOI: 10.1038/s41598-023-50978-5

Key values:

• Diffusion coefficient for LiF at 300 K (new FF): 3.44 × 10⁻⁸ cm²/s
• Improvement over prior FFs: ~1000×
• CMA-ES iterations to convergence: 13,000
• DFT simulations in training database: 300+

Running Locally

pip install -r requirements.txt
streamlit run app.py