HorizON：面向有机电池性能预测的多视界深度算子网络
HorizON: A Multi-Horizon Deep Operator Network for Property Prediction of Organic Battery Stacks
一篇电池材料方向的论文，HorizON 能预测有机电池每个循环的容量变化，而不是只给一个平均值，做电池计算材料学的朋友可以看看。
arXiv 论文提出 HorizON，一个多保真多视界深度算子网络，用于有机电极材料（OEMs）电池堆的性能预测。相比此前只预测平均放电电压和首循环比容量两个单值目标，HorizON 能预测放电轨迹中每个循环的比容量，实现长期性能预测。模型在同一框架内学习 DFT 氧化还原电位和随循环变化的比容量轨迹，并以完整电池堆表征和使用条件作为输入，而非只看正极活性材料。该工作可支持有机电池系统的逆向设计与工艺优化。
HorizON: A Multi-Horizon Deep Operator Network for Property Prediction of Organic Battery Stacks
Transition-metal-based batteries rely on critical elements such as lithium, cobalt, and nickel. Their extraction creates environmental and supply-chain challenges. Organic electrode materials (OEMs) reduce this reliance and provide broad molecular tunability. However, OEM-based batteries still show limited long-term cycling stability. Their design also requires the joint optimization of coupled metrics such as voltage, specific capacity, capacity retention, and energy density. These metrics depend on the full battery stack and on the interactions among its components rather than on the active material alone. In our previous work, we developed a data-fusion multi-task framework for average discharge voltage and first-cycle specific capacity. Both targets are single-valued. Here, we extend that framework to multi-horizon prediction by predicting a property at every cycle of a discharge trajectory rather than at a single cycle. Our multi-fidelity and Multi-horizon Deep Operator Network learns DFT redox potentials and cycle-dependent specific-capacity trajectories within one model. We train it on complete battery-stack representations and usage conditions, so it learns battery-stack interactions rather than cathode-only effects. This framework supports long-term property prediction, inverse design, and process optimization for organic battery systems.