HorizON: A Multi-Horizon Deep Operator Network for Property Prediction of Organic Battery Stacks
Abstract
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.
Related
- An Adaptive-Horizon Graph Neural Operator for coupled surrogate morphodynamic modelling
- Partial Multistep Prediction of Chaotic Time Series: What Remains Predictable beyond the Horizon of Predictability?
- GREEN-PRECONDITIONED DYNAMIC GRAPH NEURAL OPERATOR FOR ELECTROMAGNETIC FIELD PREDICTION IN HETEROGENEOUS MEDIA
- PINNIES: An efficient physics-informed neural network framework for integral operator problems
- Horizon-Aware Early Event Prediction for Tokamak Disruption Alarms
BibTeX
@misc{ganti2026horizon,
title = {{HorizON: A Multi-Horizon Deep Operator Network for Property Prediction of Organic Battery Stacks}},
author = {Subhash V. S. Ganti and Lukas Woelfel and Arla Diengdoh and Tzvetan Mihaylov and Thomas Nevolianis and Jan Meyer and Christopher Kuenneth},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.36860},
eprint = {2610.36860},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.36860},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - HorizON: A Multi-Horizon Deep Operator Network for Property Prediction of Organic Battery Stacks
AU - Subhash V. S. Ganti
AU - Lukas Woelfel
AU - Arla Diengdoh
AU - Tzvetan Mihaylov
AU - Thomas Nevolianis
AU - Jan Meyer
AU - Christopher Kuenneth
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.36860
UR - https://arxiv.org/abs/2610.36860
AB - 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.
ER -