Themed collection MSDE Open Access Spotlight

Computer-aided screening and design of metal–organic frameworks for carbon capture shift to closed-loop, optimization-guided discovery with incorporation of process-level considerations.

Luminol and its derivatives have emerged as powerful chemiluminescent agents with broad applications in biomedical diagnostics, forensic science, and environmental monitoring.

Different simulation approaches have been applied to understanding and predicting key features of the solution behavior of intrinsically disordered proteins.

We present an accurate and efficient screening protocol for azobenzene derivatives for Molecular Solar Thermal (MOST) applications based on ground state properties.

Dually-labelled polymeric micelles allowed tracing both the probe location and the levels of singlet oxygen (¹O₂) in biological systems.

Selective cysteine-to-serine mutations improves enzymatic stability and therapeutic efficacy of the immunomodulatory enzyme indoleamine 2,3-dioxygenase.

Pre-polymerization MD with SASA/FFV analysis predicts solvent-driven porosity in MIPs, matching experiments and SEM. This approach enables rational tuning of polymer surface area by solvent choice.

The thermal stability of CRISPR-Cas nucleases is a critical factor for their successful application in ‘one-pot’ diagnostic assays that utilize high-temperature isothermal amplification.

Confinement catalyzes the crystallization and flat interfaces increase the crystallinity of binary crystals self-assembled from hard colloidal tetrahedra and octahedra, simulated with Monte Carlo simulations.

Incorporating 60% recycle into urea–microalgae mixtures increased the yield of commercial-sized granules (2.0–4.0 mm) to about 68%, with DEM-based molecular modelling confirming its role in optimizing fertilizer production.

This study explores low-dimensional active subspace of pre-trained VAE-based generative molecular design models to perform uncertainty-guided fine-tuning of the VAE parameters to enhance downstream design task performance of the pre-trained models.

A hybrid method for reliable property prediction with uncertainty quantification that integrates group contribution with Gaussian process regression.

Twist-and-lock assembly of supramolecular gel noodles creates hierarchical filaments that combine strength and function. We studied twisting mechanics, crossover spacing, tensile strength, and stimuli-responsiveness to enable targeted applications.

Field-theoretic simulations can be stabilized and accelerated through judiciously chosen relaxation coefficients. We demonstrate how these optimal coefficients can be obtained using Bayesian optimization.

Boron subphthalocyanines (BsubPcs) are a class of macrocycles that are often stable and straightforward to synthesize.

This work reveals the intricate interplay between curvature, shell thickness, and anchoring asymmetry in governing structural transitions, dynamic behavior, and defect morphologies in highly chiral cholesteric liquid crystal shells.

A topologically diverse MOF dataset, NU-topoMOF-2025, was constructed and screened to identify topology-based design rules for materials with enhanced hydrogen storage performance.

Grain boundaries for systems composed of patchy colloids with a periodic order and in aperiodic structures.

Unlocking the selective adsorption of NO, NO₂ and CO in the presence of H₂O and CO₂ using iron zeolites: a systematic DFT screening of key questions and properties.

Nanoclay enhances the actuation of thermally-responsive 3D-printed hydrogel bilayers.

Apart for the case of noble gases, no single set of molecular force field parameters can simultaneously describe the fluid phase equilibria, transport, and solid transition properties of quasi-spherical molecules.

We extended GAFF parameters for porphyrin macrocycles and obtained TPPS₄ tetramers trajectories, which shows the emergence of global tetramer structure curling.

Peptide–polyurea (PPU)/cellulose nanocrystal (CNC) nanocomposites demonstrate hierarchical assembly into non-covalent hydrogels with tunable strength and rapid shear recovery.

Developments related to large language models (LLMs) have deeply impacted everyday activities and offer the potential to automate complex research workflows involving repetitive and time-consuming tasks. The presented template enables the rapid incorporation of custom tools with LLM agents.

In this work, we have investigated the semiconducting properties of an unprecedented 1 : 1 π-stacked donor–acceptor cocrystal of 1,5-dihydroxynaphthalene (DHN) as the π-donor (D) with 7,7′,8,8′-tetracyanoquinodimethane (TCNQ) as the π-acceptor (A).

Self-assembled peptide amphiphile micelles act as nanostructured, modular drug delivery vehicles whose architectures can be tuned for efficient transport of therapeutic peptides for a multitude of applications.

Different eutectic solvent components are simulated to understand the thermodynamics associated with the different, potential CO₂ complexation pathways.

An organic cage-based porous ionic liquid (PIL) was constructed by functionalising a reduced cage core with ionic liquid functionality. The resulting PIL displayed melting behaviour, a glass phase, enhanced CO₂ uptake and permanent porosity.

Transfer learning followed by density functional theory accelerates material discovery of conjugated oligomers for high-efficiency organic photovoltaic materials.

Computational models of polymer adhesion reveal how pulling speed, interfacial kinetics and detachment mechanisms influence performance.

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors.

Crystal-site and non-crystal-site residues in crystal interfaces are classified using machine learning and in silico modeling to identify key structural physicochemical features influencing fragment antigen-binding (Fab) crystallization.

This paper highlights a bioinspired approach to engineering water-responsive materials via a diverse array of self-assembled nanostructures.

Backmappings of protein sidechains exposing exact probability densities of generated configurations enable reweighting with protein force fields. Though trained models produce low-energy configurations, reweighting remains unexpectedly challenging.

We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design.

Ion-conductive 2D nanostructured liquid crystals containing linear carbonate moieties are developed. The complexes of these materials with lithium salts may have potential as self-assembled electrolytes in lithium-ion batteries.

Integrating a thermodynamic model for solid–fluid interactions into a process model to screen nanoporous materials for carbon capture.

Soft porous coordination polymers show metastable states in volume while varying loading. The flexibility of the linkers affects the resulting configurations.

Peptides are a powerful class of molecules that can be applied to a range of problems including biomaterials development and drug design.

The relationship between the design of macromolecular inks and material properties of two-photon 3D laser printed structures is investigated.

We develop a physics-informed machine learning workflow that accelerates multicomponent phase-coexistence calculations on the number, composition, and abundance of phases. The workflow is demonstrated for systems described by Flory–Huggins theory.

We outline a method for synthetically generating negative data by considering alternative reaction centers on small-molecule substrates that are known to participate in enzymatic reactions.

New diol chemistries are explored for insulin delivery from glucose-responsive hydrogels comprised of dynamic-covalent crosslinking interactions between phenylboronic acids and diols.