Machine learning is revolutionising materials modelling but requires high-quality training data. Here, the authors introduce autoplex, an open framework automating exploration and fitting of potential...

A high-sensitivity, broadband, transient, full-Stokes spectroscopy setup is demonstrated, which can detect quickly varying small signals from chiral emitters.

Machine-learned interatomic potentials have transformed computational research in the physical sciences. Recent atomistic `foundation' models have changed the field yet again: trained on many differen...

The chalcogenide perovskite material BaZrS$_{3}$ is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to describe the material's ...

The vastness of chemical space makes discovering new materials challenging. Here, authors propose a generative AI model enabling crystal structures generation from textual descriptions, accelerating m...

A complete series of CsPb(ClxBr1−x)3 mixed-halide perovskites with x = 0–1 in small steps is reported, and their structural and optical properties characterised. A comparison of synthetic approaches s...

Conflicting theories exist on the structure of amorphous silicon. Here the authors use machine-learning-driven molecular dynamics to show that amorphous Si can accommodate a degree of local paracrysta...

Endowing chirality onto solution-processable semiconductors unlocks new bounds for their spin and optoelectronic properties. This Review discusses the synthesis, properties, characterization methods, ...

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of...

Sugars are ubiquitous in biology; they occur in all kingdoms of life. Despite their prevalence, they have often been somewhat neglected in studies of structure–dynamics–function relationships of macro...

Official implementation of MatterGen -- a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property c...

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Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interato...

Nature Reviews Materials - Amorphous materials are increasingly central components of key technologies, but their structures remain challenging to study. This Perspective highlights how recent...