Investigating the structure, transport properties, and mechanisms of battery materials using physics-informed machine learning and enhanced sampling techniques.
Overview
My research in energy storage materials focuses on understanding and predicting the behavior of next-generation battery components at the atomic and molecular levels. I employ a multifaceted approach combining classical molecular dynamics, rare event sampling, and machine learning to gain insights that can lead to more efficient, longer-lasting, and safer energy storage solutions.
Visualization of sodium ion diffusion pathways in a disordered carbon anode material, simulated using enhanced sampling techniques.
Key Research Directions
Sodium-Ion Battery Materials
I investigate sodium storage mechanisms in disordered carbon materials, which are promising candidates for next-generation batteries due to their abundance and lower cost compared to lithium-based systems. My work focuses on:
- Developing physics-informed machine learning models to predict Na+ ion behavior in complex carbon structures
- Characterizing the atomic-scale mechanisms of sodium insertion and extraction
- Identifying key structural features that enhance sodium storage capacity and kinetics
This research provides fundamental insights that can guide the design of improved sodium-ion battery electrodes with higher energy density and faster charging capabilities.
Battery Electrolytes at Extreme Conditions
Electrolyte performance at extreme temperatures remains a critical challenge for many battery applications. My research in this area includes:
- Simulating electrolyte behavior at temperatures ranging from -40°C to 80°C
- Investigating ion transport mechanisms and how they change with temperature
- Predicting electrolyte stability and degradation pathways under extreme conditions
This work aims to develop electrolyte formulations that maintain performance across a wider temperature range, enabling batteries that can operate reliably in diverse environmental conditions.
Electrode-Electrolyte Interfaces
The solid-electrolyte interphase (SEI) plays a crucial role in battery performance and longevity. My research examines:
- Formation mechanisms of the SEI layer at various electrode materials
- Ion transport across complex interfacial regions
- Strategies to control and optimize interfacial properties for enhanced battery performance
By understanding these interfacial processes at the molecular level, I aim to develop approaches that can extend battery life and improve safety.
Methodology
My research employs cutting-edge computational techniques:
- Classical Molecular Dynamics (CMD) - For large-scale simulations of battery materials and interfaces
- Enhanced Sampling Methods - Including metadynamics and umbrella sampling to access longer timescales relevant to battery processes
- Machine Learning Potentials - Development of neural network potentials that combine quantum mechanical accuracy with classical MD efficiency
- Physics-Informed Neural Networks - Ensuring that machine learning models respect fundamental physical principles
Impact
This research addresses critical challenges in energy storage technology that have implications for renewable energy integration, electric vehicles, and portable electronics. By gaining a deeper understanding of the fundamental processes in battery materials, my work contributes to the development of next-generation energy storage solutions that are more sustainable, efficient, and reliable.
Related Publications
Select publications related to energy storage materials research:
Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon
Energy Storage Materials, 104967, 2026
View All Energy Storage Publications