ActMOF addresses the challenge of optimizing metal-organic framework (MOF) syntheses when experimental data are sparse and outcomes noisy, by introducing an experimentally grounded benchmark for comparing active-learning policies. Starting from only 95 measured microwave syntheses of an aluminum-carboxylate MOF, the authors emulate a landscape of over six million encoded conditions and evaluate 39 policies—Gaussian processes, tree ensembles, and large language models—before transferring the best strategy to six wet-lab syntheses. The demonstration that all six campaigns yielded crystalline products within a 12-experiment budget makes the work relevant as both a reproducible testbed and a proof of concept for transferable, AI-guided reticular chemistry.

Bridging quantum-mechanical accuracy with mesoscopic length and time scales remains a grand challenge in materials modeling. This paper develops an ab initio phase-field framework that derives the mesoscopic evolution equation from molecular dynamics via Mori–Zwanzig projection, leaving only the free energy and mobility to be learned by neural networks from short machine-learning-interatomic-potential trajectories. The result is a data-efficient approach that propagates ab initio accuracy to scales far beyond direct atomistic simulation, demonstrated here on iron–boron melts and hydrogen–helium mixtures under planetary conditions.

Sodium-ion batteries need high-capacity anodes with fast ion transport, but hard carbon suffers from structural disorder and slow diffusion. This computational study uses the SpookyNet machine-learning force field with DFT to characterize aminobenzene-functionalized Janus graphene at room temperature. The work identifies a three-stage sodium storage mechanism and predicts a high capacity of ~400 mAh g$^{-1}$ with diffusion coefficients two to three orders of magnitude above hard carbon.