Machine Learning Potentials (MLPs) for Accelerated Battery Electrolyte Molecular Dynamics

• Understand the limits of DFT and AIMD and how AI bridges the scale gap.
• Train and validate Machine Learning Potentials using MLP frameworks.
• Deploy MLPs for large-scale molecular dynamics simulations.
• Analyze battery performance metrics: ionic conductivity, MSD, and diffusion coefficients.
• Integrate AI workflows into publication-ready and reproducible pipelines.

Day 1: Quantum to AI – Data Curation & Interatomic Potential Fitting

• Focus: Data engineering for materials and bridging DFT data with Machine Learning frameworks.
• The computational limits of ab initio Molecular Dynamics (AIMD) in battery interface modeling.
• Architectural overview of modern MLPs: Descriptor-based (SNAP), Graph Neural Networks (CHGNet), and Equivariant GNNs (MACE).
• Data preprocessing workflows: Extracting energies, forces, and stresses using the Atomic Simulation Environment (ASE).
• 🚀 Hands-on Lab (Google Colab): Dataset to Potential Import a raw DFT dataset for a lithium-ion solid electrolyte, preprocess the structural configurations using ASE, and initialize an equivariant machine learning model for training.

Day 2: High-Performance Training with DeepMD-kit

• Focus: Training, optimizing, and validating neural network potentials for production-grade accuracy.
• DeepMD-kit Architecture: Multi-task learning routines for mapping atomic coordinates to total energy and forces.
• Hyperparameter optimization: Balancing loss function weights ($E$ vs. $F$ vs. $V$) and managing learning rate decay.
• Error analysis for high-impact publishing: Evaluating Root Mean Square Error (RMSE) against validation benchmarks.
• 🚀 Hands-on Lab (Google Colab): Training the Neural Network Configure a DeepMD-kit training pipeline using a JSON framework, execute a multi-epoch training run on a cloud GPU, and analyze the loss convergence behavior.

Day 3: Large-Scale Production MD Simulation via LAMMPS

• Focus: Deploying trained MLPs into scalable, real-world molecular dynamics production runs.
• Interfacing trained ML models into LAMMPS for high-performance computing.
• Configuring large-scale ensembles (NVT/NPT) for complex solid-state electrolyte interfaces.
• Post-processing trajectories: Extracting critical battery metrics including Lithium-ion Conductivity and Mean Squared Displacement (MSD).
• 🚀 Hands-on Lab (Google Colab): The Million-Atom Simulator Deploy your trained ML potential directly into a LAMMPS execution block, simulate a large-scale solid electrolyte system, and calculate the target ionic diffusion coefficients.

• Gain hands-on experience in preprocessing DFT datasets for machine learning potential training.
• Train and validate MLPs (SNAP, MACE, CHGNet, DeepMD-kit) for atomic-scale simulations.
• Scale molecular dynamics simulations from hundreds to hundreds of thousands of atoms.
• Analyze battery-relevant properties such as ionic conductivity, mean squared displacement (MSD), and diffusion coefficients.
• Apply AI workflows to accelerate research pipelines and generate publication-ready results.
• Deploy trained potentials in LAMMPS for high-performance, large-scale production runs.
• Integrate cloud-based GPU computing via Google Colab to run intensive simulations without local HPC infrastructure.

• Researchers, PhD scholars, and postdocs in Materials Science, Computational Chemistry, Physics, or Chemical Engineering.
• Industry R&D professionals working on battery materials, electrochemistry, or computational modeling.
• Individuals with basic Python and molecular modeling knowledge seeking hands-on AI-driven simulation skills.