Standard deep learning for spectroscopy treats spectra as fixed-grid intensity vectors, which makes models brittle when sampling grids, spectral ranges, or instruments change. This paper proposes to supply each spectral point as a pair of intensity and physical wavenumber, computing Transformer positional encodings directly from the wavenumber coordinate rather than the array index. Evaluated on in-house bacterial Raman spectra and a public 30-isolate benchmark, the approach improves robustness under partial spectral observation and on sampling grids never seen during training.

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.

Equivariant machine learning interatomic potentials are held back by costly tensor product operations that dominate inference at scale. BranchIP addresses this by introducing lightweight, learned routers inside a single model that adaptively terminate tensor product refinement for individual features, trained with a novel distillation loss. On two realistic reactive systems—CO/Pt catalysis and CsH2PO4 proton conductors—it delivers up to 2.4× inference speedup and 2.6× memory reduction while preserving exact energy conservation and offering chemically interpretable routing patterns.

SupraTITO tackles the costly problem of predicting how peptide sequences drive supramolecular self-assembly. It learns transferable implicit transfer operators conditioned on sequence, topology, and periodic boundary geometry to generatively propagate many-molecule configurations over long physical lags. Evaluated on 400 coarse-grained dipeptide systems, it generalizes to held-out sequences and unseen concentrations while preserving molecular integrity through recursive rollouts. The work extends generative molecular dynamics from single-molecule conformations to collective periodic assembly.

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.

Suiren-1.0 introduces a family of molecular foundation models designed to bridge the gap between microscopic 3D quantum-mechanical conformations and macroscopic 2D molecular property prediction. The framework comprises Suiren-Base (a 1.8B-parameter SE(3)-equivariant GNN pre-trained on 70M DFT samples), Suiren-Dimer (continued pre-training on intermolecular interactions), and Suiren-ConfAvg (a lightweight 2D model distilled via a novel Conformation Compression Distillation diffusion framework). This work matters because it attempts to unify quantum-accurate representations with practical cheminformatics workflows where only SMILES or graph inputs are available.