Novel Battery Materials Design and Discovery

Unsupervised machine learning for detecting mutual independence among eigenstate regimes in interacting quasiperiodic chains

MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials

Theoretical Design of Single/Double Atom Catalysts based on high-throughput calculations and machine learning

Elucidating Gas Reduction Effects of Organosilicon Additives in Lithium-Ion Batteries

Interpretable and Controllable Generative Models and Their Applications in Materials Science

Computational design of sustainable 2D semiconductors, interfaces and devices for future computing

Efficient Multi-Domain Training Strategy for Universal Machine Learning Interatomic Potentials

Moment Tensor Machine Learning Potential: Development and Applications

Development and Application of Catalytic Large Atomic Model based on Machine Learning Potentials

AI Empowered Thermal Management Materials Design

Machine learning empowered material atomic structural understanding

Computational profiling of novel metastable interfacial phases

Scalable Discovery of Ferroelectrics with High-Throughput DFT and AI Techniques

Theoretical study on controllable synthesis of 2D materials

reEWC: A Forgetting-Aware Fine-Tuning Framework for Pretrained Universal Machine-Learning Interatomic Potentials