Active learning guides better microwave synthesis of metal-organic frameworks
ActMOF: Mapping Reticular Synthesis through Active Learning
Machine Learning
Summary
Making new metal-organic frameworks (MOFs) is tricky because experiments have limited data and unpredictable results. The authors created ActMOF, a tool that uses past experiment data to test different strategies for planning new experiments to get better crystals. They found that certain strategies based on math models worked best and could help make new MOFs with fewer experiments. This work offers a way to improve designing MOFs using artificial intelligence.
What this means in practice
- •For materials scientists: Use ActMOF as a testbed to design efficient experimental plans for synthesizing new aluminum-based MOFs with fewer trials.
- •For chemical automation teams: Integrate ActMOF-guided strategies to optimize automated microwave synthesis setups, improving yield and success rates for MOFs.
Authors
Hongrui Sheng, Shutian Liang, Reid A. Coyle, Shyam Chand Pal, Peter Walther, Bin Feng, Xinyu Ding, Zhiling Zheng
Abstract
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 encoded condition assigned a crystallinity index calculated based on diffraction peak intensity and full width at half-maximum. By benchmarking policies guided by Gaussian processes (GP), tree ensembles and large language models (LLM), ActMOF placed the GP family among the leading policies for active learning in sparse synthesis landscapes. It then demonstrated transferability by guiding the microwave synthesis of six reticularly related aluminum MOFs. All six campaigns gave crystalline products within a fixed budget of 12 experiments each, and established a reusable testbed for AI-driven MOF synthesis.