HorizON predicts cycle-by-cycle properties of organic battery stacks
Original title: HorizON: A Multi-Horizon Deep Operator Network for Property Prediction of Organic Battery Stacks
One multi-fidelity model learns DFT redox potentials and specific-capacity trajectories from full stack representations.
The authors extend an earlier multi-task model of average discharge voltage and first-cycle specific capacity to multi-horizon prediction. HorizON, a multi-fidelity Multi-horizon Deep Operator Network, predicts a property at every cycle of a discharge trajectory rather than a single value. It is trained on complete battery-stack representations and usage conditions so it captures interactions among components, not cathode-only effects. The stated uses are long-term property prediction, inverse design, and process optimization for organic electrode batteries.
- Prior targets were single-valued average voltage and first-cycle specific capacity.
- Organic electrodes are framed as reducing reliance on lithium, cobalt, and nickel.
- OEM batteries still have limited long-term cycling stability, the paper says.
- Predicted metrics include voltage, capacity, retention, and energy density dependencies.
From the original article
1 min read at arxiv.orgTransition-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.
By Subhash V. S. Ganti · arxiv.org
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