jwlimgroup

Research

Li-ion battery (LIB) cathodes (LFP, NMC)

LFP offer lots of benefits compared to lead-acid batteries and other lithium batteries such as long life span, no maintenance, extremely safe, lightweight, improved discharge and charge efficiency. Also, high Nickel NMC and Mn-rich layered oxides are commercially the most interesting candidates for the premium LIB for electric vehicles. We directly collaborate with the many leading industry partners to commercialize the high-energy LIB.

We investigate Li ion insertion kinetics using various LIB cathode materials. The local current and lithium concentration within the individual battery active particles determines the local stress and cycle life. Our group develops state-of-art x-ray microscopy techniques to take the video of lithium movement at nanometer scale.

Learn more

Li metal batteries

Non-fluorinated diluents offer a sustainable choice for localized high-concentration electrolytes, but their vast chemical space makes purely experimental screening prohibitively time- and cost-intensive. To navigate this space efficiently, we establish physically meaningful computational descriptors that quantitatively capture critical molecular interactions and chemical properties. We then construct a comprehensive dataset through quantum chemical calculations and train an uncertainty-aware machine learning model based on molecular graphs. This model provides both target property predictions and calibrated confidence estimates to ensure high screening reliability. Utilizing this uncertainty-guided high-throughput framework, we successfully screen the extensive chemical space to identify highly promising non-fluorinated diluent candidates.

Learn more

All-solid-state batteries (ASSBs)

All-solid-state batteries (ASSBs) are widely regarded as next-generation energy storage systems because of their high energy density and enhanced safety. However, the confinement of solid electrolytes and electrodes within a rigid cell architecture generates complex internal stresses that remain difficult to understand and control. Our research addresses key barriers to ASSB commercialization, including moisture instability, interfacial degradation, and low-pressure fabrication and operation. To overcome these challenges, we combine advanced characterization techniques, such as synchrotron-based X-ray analysis, with innovative manufacturing approaches including warm isostatic pressing and roll pressing. We also develop novel sulfide and halide solid electrolytes and engineered silicon-based anode materials.

Learn more

Artificial intelligence, machine learning

Batteries have become indispensable to modern energy systems. However, understanding and accelerating their development remains a challenge. Battery research involves high-dimensional data and vast chemical spaces that are difficult to fully explore through experiments alone. Thus, AI(artificial intelligence) is a powerful tool to complement traditional investigation. We extract meaningful features from electrochemical signals to predict capacity and lifetime, and identify key molecular descriptors to efficiently navigate large chemical spaces in search of better electrolytes for lithium metal batteries. Watch us how we take on these challenges through AI-driven battery research.

Learn more

Computation (First-principles calculations)

Designing next-generation battery materials requires navigating an enormous design space defined by materials chemistry, physical properties, and structural variables. Our group addresses this challenge through an integrated computational framework that spans atomistic simulation, machine learning, and autonomous experimentation. At the atomistic level, we employ Density Functional Theory (DFT), Ab initio Molecular Dynamics (AIMD), and Machine-Learned Interatomic Potentials (MLIP) to investigate the fundamental properties of battery electrodes and electrolytes, from electronic structure and Li⁺ migration barriers to large-scale ionic transport and long-time structural evolution. To bridge the gap between atomic-scale understanding and materials-level performance, we develop machine learning models that predict key properties including lifetime, electrochemical performance, and optimal electrolyte composition, enabling high-throughput screening across vast compositional spaces. Ultimately, these computational insights are seamlessly integrated into a Self-Driving Laboratory (SDL) platform, where automated electrolyte synthesis, real-time electrochemical data acquisition, and closed-loop AI-driven optimization work in concert to accelerate the discovery and validation of superior battery materials. By connecting first-principles physics to autonomous experimentation, our group aims to fundamentally transform how battery materials are designed and discovered.

Learn more

Electrocatalysts

Carbon capture and utilization (CCU) is drawing attention around the world as a way to solve the problem of greenhouse gas emissions and increasing energy demands. Among various efforts, the conversion of CO2 into fuels and chemical feedstocks via electrochemical methods is a representative technology of CCU.

We investigate the dynamic phase evolution of Cu-based electrocatalysts during CO2 reduction to elucidate the origin of their activity and selectivity. Using operando soft X-ray microscopy, we revealed the dynamic evolution of cationic Cu species and their role in enhancing C-C coupling activity. We also demonstrated that dilute p-block metal doping (In, Sn) into Cu2O enhances CO2-to-CO conversion efficiency and stability by modulating the electronic structure and stabilizing Cu+ species.

Learn more