Volume 6 Issue 3, March 2026

After years of progress, density functional theory is entering a period of rapid advancement, enabled by emerging generalized schemes, richer descriptors, machine learning, and the anticipated development of broader, higher-quality datasets.

The PropMolFlow model uses flow matching to efficiently generate chemically valid molecules in three dimensions with targeted properties, enabling accelerated discovery of molecules useful in materials and pharmaceutical science.

A reward function (TANGO) is developed to enforce building blocks in generative artificial intelligence and leverage the synthesizability of high-value materials.

A machine learning framework reveals how dynamic routing and interpretability can accelerate the discovery of better electrolytes for next-generation batteries.

We developed an open-source, prototype AI collaborator for the science of science (SciSci). Through a web-based chat interface, SciSciGPT orchestrates auditable, automated workflows for literature understanding and data processing, analytics, and visualization. The system accelerates early-stage idea exploration, prototyping, and iteration, while improving reproducibility and accessibility for SciSci researchers.

PropMolFlow is a flow-matching method for property-guided molecule generation that matches diffusion model performance while generating stable, valid structures more quickly and enabling the discovery of molecules with under-represented property values.

A hierarchical cross-entropy loss is presented, which incorporates ontology structure into training and improves the out-of-distribution performance of large-scale single-cell annotation models without additional computational cost.

deepmriprep leverages neural networks to enable voxel-based morphometry preprocessing of MRI data that is 37× faster than existing methods while achieving comparable accuracy in segmentation, registration and final statistical maps across large datasets.

The authors propose the TANGO reward function, which enables the generation of property-optimized small molecules with predicted synthesis routes, incorporating a small set of shared precursors.

This study presents a dynamic routing-guided framework to model and interpret salt–solvent chemistry, which effectively handles long-tailed data and captures the full spectrum of formulations, shaping the conductivity atlas for non-aqueous electrolytes.

APOLLO is an autoencoder-based framework to integrate diverse data modalities while preserving both shared and modality-specific information. It enables predicting missing data modalities and identifying the influence of each modality on a phenotype.

SciSciGPT is an open-source prototype AI collaborator that explores the use of LLM research tools to automate workflows, support diverse analytical approaches and enhance reproducibility in the domain of science of science.