5 papers
Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery
Weixiang Hong, Hongting Du, Jiayue Tang +4
Electrolyte additive discovery remains challenging because experimentally validated molecules are sparse, whereas accessible chemical spaces are vast and largely unlabeled. This ch…
BatteryMFormer: Multi-level Learning for Battery Degradation Trajectory Forecasting
Ruifeng Tan, Jintao Dong, Weixiang Hong +3
Early battery degradation trajectory forecasting (BDTF), which predicts the full-life state-of-health trajectory from early operational data, is critical for battery optimization,…
Pretrained battery transformer (PBT): A foundation model for battery life prediction
Ruifeng Tan, Weixiang Hong, Jia Li +2
Early prediction of battery cycle life is essential for improving battery design, manufacturing and deployment. However, despite encouraging progress with machine learning, battery…
BatteryLife: A Comprehensive Dataset and Benchmark for Battery Life Prediction
Ruifeng Tan, Weixiang Hong, Jiayue Tang +6
Battery Life Prediction (BLP), which relies on time series data produced by battery degradation tests, is crucial for battery utilization, optimization, and production. Despite imp…
SimXRD-4M: Big Simulated X-ray Diffraction Data Accelerate the Crystal Symmetry Classification
Bin Cao, Yang Liu, Zinan Zheng +3
Spectroscopic data, particularly diffraction data, contain detailed crystal and microstructure information and thus are crucial for materials discovery. Powder X-ray diffraction (X…