ActMOF: Mapping Reticular Synthesis through Active Learning
Reticular chemistry allows metal-organic frameworks (MOFs) to be designed from their building units, yet their synthesis remains constrained by sparse experimental data, noisy crystallization outcomes, and the absence of benchmarks for comparing active-learning strategies. While data-driven approaches such as Bayesian optimization (BO) can improve synthesis success rates, selecting appropriate optimizers beforehand for mapping MOF crystallization landscapes remains largely empirical. Here we present ActMOF, an experiment-grounded benchmark for comparing search strategies in aluminum-carboxylate MOF synthesis. ActMOF is emulated from experimentally measured microwave syntheses with each en...
| Authors | Hongrui Sheng, Shutian Liang, Reid A. Coyle et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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