Lipid nanoparticles are now going into billions of arms in the form of COVID-19 mRNA vaccines, marking a historical milestone for the drug delivery and nanomedicine communities. Lipid nanoparticle-mRNA vaccines against other infectious diseases, cancers and genetic disorders are also on the horizon, and other organic and inorganic nanoparticles are being explored, both preclinially and in clinical trials, for a variety of applications. See Lasting impact of lipid nanoparticles

Low-nuclearity catalysts incorporating supported metal atoms or small clusters on appropriately tailored carriers are growing in diversity and have great potential in catalysis. Their synthesis and characterization are progressing towards the atomically precise design of high-performing new architectures. See Sharon Mitchell & Javier Pérez-Ramírez.

Quantum bits (qubits) hold promise for the realization of quantum computers, which will surpass classical computers for specific tasks, such as searching large databases or performing quantum chemical computations. Moving forward, materials optimization will be instrumental to improve the performance and scalability of qubits and enable the realization of practical quantum computers. In this focus issue, our collection of articles explores the materials-related challenges and opportunities for different types of qubits, including superconducting, trapped-ion, spin, germanium and topological qubits. See Qubits meet materials science.

Nanomaterials can be engineered to exploit different nano–bio interactions with a single system. Such multifunctionality is particularly important for the treatment of complex and heterogeneous diseases, such as cancer. Biomolecule-based nanostructures, including polysaccharides, nucleic acids, peptides and proteins, are often intrinsically bioactive as targeting and/or therapeutic agents, making them ideal building blocks for the design of smart cancer nanomedicines. See Wang, J. et al.

Machine learning is a powerful tool in materials research. In this Focus Issue, our collection of articles looks in depth at applications of machine learning in various areas of materials science ‒ from the design of photonic devices and the optimization of alloys, to the engineering of high-performance polymers and nanoparticles. We also highlight how machine learning algorithms enable the interrogation of complex and large biomedical datasets, and explore synergies between computational sustainability and materials science. See Rise of the machines

Polarons — quasiparticles arising from the interaction of electrons with lattice vibrations — manifest themselves in many different ways and have a profound impact on materials properties and functionalities. Polarons have been the testing ground for the development of numerous theories, and their manifestations have been studied by many different experimental probes. See Franchini et al.

Reticular chemistry promotes the discovery of periodic solids, such as metal–organic frameworks. Cutting-edge structural design methodologies, using diverse building blocks, targeted nets and isoreticular chemistry, are providing access to new and more intricate structures. See Jiang et al.

Colloidal quantum dots (CQDs) are promising materials for realizing versatile, wavelength-tunable, solution-processed lasers. Other benefits offered by CQDs for lasing applications include low optical-gain thresholds and high temperature stability of the lasing characteristics. See Park et al.

Materials synthetic biology integrates synthetic biology and materials science for the development of self-organizing functional materials and hybrid living materials. These dynamic and responsive materials can be applied for the design of living sensors, therapeutics, electronics, energy-conversion materials and living building materials. See Tang et al.

Metamaterials provide a platform to leverage optical signals for performing specific-purpose computational tasks with ultra-fast speeds. This Review surveys the basic principles, recent advances and promising future directions for wave-based-metamaterial analogue computing systems. See Zangeneh-Nejad et al.

Non-metallic charge carriers provide an alternative to metallic charge carriers in aqueous batteries, enabling fast kinetics, long cyclic lifetime and low manufacturing costs. This Review discusses different non-metallic charge carrier species, charge storage mechanisms and battery operation configurations. See Liang et al.

π-Conjugated polymers possess a wide range of useful electronic and optical properties. This Review focuses on the preparation of self-assembled nanoparticles from these polymers and their applications in areas such as optoelectronics, biomedical imaging and therapy, photocatalysis and sensing. See MacFarlane et al.