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Fully fledged quantum computers are still a long way off. But devices that can simulate quantum systems are proving uniquely useful.

Researchers search for ways to put today’s small noisy quantum systems to work.

Sustainability and artificial intelligence dominate our seventh annual round-up of exciting innovations.

US ‘national drama’ drives theoretical chemist to move to Canada.

Atomistic simulations are vital for computational chemistry and materials science, but their adoption remains challenging due to the need for expert knowledge and manual effort for the setup, execution, and validation stages. Here, the authors present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows.

Artificial intelligence has recently seen numerous applications in synthetic organic chemistry. Advanced pattern-recognition heuristics may facilitate the access to chemical matter of interest and complement chemical intuition in the near future.

Here the authors report NiGa₂O_4–x(OH)_y for light-driven CO₂ hydrogenation to methanol. The surface Lewis acid–base pairs and -OH groups act as conduits for H^- /H⁺ transport to active sites, enhancing photocatalytic methanol production.

Generative models face challenges with low synthesizability of generated molecules. Here, authors develop ClickGen, a deep learning model using modular reactions and reinforcement learning to generate highly diverse, novel, and synthesizable molecules

From genius grids to sassy storage, three-dozen experts figure out the next-generation power puzzle.

Topological phases are unusual states of matter whose properties are robust against small perturbations. Using a photonic quantum walk system, Kitagawaet al. simulate one-dimensional topological phases and reveal novel topological phenomena far from the static or adiabatic regimes.

Redox-flow batteries with organic-based electrolytes hold many advantages over conventional-flow batteries. Here the authors report a high-performance flow battery based on alloxazine, an aqueous-stable and soluble redox-active organic molecule resembling the backbone structure of vitamin B2.

There is growing evidence that quantum coherence enhances energy transfer through individual photosynthetic light-harvesting protein complexes. This idea is now extended to complicated networks of such proteins and chemical reaction centres. A mathematical analysis reveals that coherence lengths up to 5 nm are possible.

Deep learning-based methods to generate new molecules can require huge amounts of data to train. Skinnider et al. show that models developed for natural language processing work well for generating molecules from small amounts of training data, and identify robust metrics to evaluate the quality of generated molecules.

To mark the occasion of Nature Chemistry turning 10 years old, we asked scientists working in different areas of chemistry to tell us what they thought the most exciting, interesting or challenging aspects related to the development of their main field of research will be — here is what they said.

A novel covalent inhibitor, ISM3312, targets the main protease of multiple human coronaviruses, including drug-resistant strains, and shows broad antiviral activity. It offers a promising therapeutic strategy against current and future coronavirus threats.

Users urge caution in revamp of service at the heart of physics.

Photosynthetic bacteria growing in low light environments have evolved to use small amounts of light with high efficiency. Here, Coles et al. demonstrate strong exciton–photon coupling of about 1,000 chlorosomes to a confined cavity mode thus modifying the energy levels of the light-harvesting process.

GAME-Net is a graph deep learning model trained with small molecules containing a wide set of functional groups for predicting the adsorption energy of closed-shell organic molecules on metal surfaces, avoiding expensive density functional theory simulations.

Machine learning is increasingly popular in materials science research. This Review generalizes learnings from applied machine learning in robotics and gameplaying and extends it to materials science. In particular, hybrid approaches combining model-based and data-driven models are seeding the transition from the application of machine learning to discrete tools and workflows towards emergent knowledge.

Launch a global clean-energy initiative to set priorities that galvanize researchers to deliver breakthroughs, write Alan Bernstein and colleagues.

Precise calculations of molecular properties from first-principles set great problems for large systems because their conventional computational cost increases exponentially with size. Quantum computing offers an alternative, and here the H₂ potential energy curve is calculated using the latest photonic quantum computer technology.

A high-throughput virtual screening approach is used to select molecules with efficient, thermally activated delayed fluorescence. The good performance of several selected emitters in organic LED applications has also been confirmed experimentally.

Artificial intelligence can speed up research into new photovoltaic, battery and carbon-capture materials, argue Edward Sargent, Alán Aspuru-Guzikand colleagues.

A machine learning model allows the identification of new small-molecule kinase inhibitors in days.

Quantum computers promise to efficiently predict the structure and behaviour of molecules. This Perspective explores how this could overcome existing challenges in computational drug discovery.

A solution-processing method known as solution shearing is used to introduce lattice strain to organic semiconductors, thus improving charge carrier mobility.

Natural photosynthetic systems harvest light to perform selective chemistry on atmospheric molecules such as CO₂. This Review discusses the implementation of bioinspired concepts in engineered light harvesting and catalysis.

Machine learning is poised to accelerate the development of technologies for a renewable energy future. This Perspective highlights recent advances and in particular proposes Acc(X)eleration Performance Indicators (XPIs) to measure the effectiveness of platforms developed for accelerated energy materials discovery.

Coherence observed in chemical and biological systems suggests that even in the presence of disorder and noise the phenomenon may yield transformative ways for improving function.

Quantum optics has played an important role in the exploration of foundational issues in quantum mechanics, and in using quantum effects for information processing and communications purposes. Photonic quantum systems now also provide a valuable test bed for quantum simulations. This article surveys the first generation of such experiments, and discusses the prospects for tackling outstanding problems in physics, chemistry and biology.

The discovery and development of advanced materials are imperative for the clean energy sector. We envision that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.