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Article
Data as the next challenge in atomistic machine learning
As machine learning models are becoming mainstream tools for molecular and materials research, there is an urgent need to improve the nature, quality, and accessibility of atomistic data. In turn, there are op...
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Open AccessModelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning
Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxid...
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Article
Open AccessGeometrically frustrated interactions drive structural complexity in amorphous calcium carbonate
Amorphous calcium carbonate is an important precursor for biomineralization in marine organisms. Key outstanding problems include understanding the structure of amorphous calcium carbonate and rationalizing it...
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Article
Open AccessDevice-scale atomistic modelling of phase-change memory materials
Computer simulations can play a central role in the understanding of phase-change materials and the development of advanced memory technologies. However, direct quantum-mechanical simulations are limited to si...
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Article
Simulations in the era of exascale computing
Exascale computers — supercomputers that can perform 1018 floating point operations per second — started coming online in 2022: in the United States, Frontier launched as the first public exascale supercomputer a...
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Article
Open AccessA machine-learned interatomic potential for silica and its relation to empirical models
Silica (SiO2) is an abundant material with a wide range of applications. Despite much progress, the atomistic modelling of the different forms of silica has remained a challenge. Here we show that by combining de...
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Open AccessAb initio molecular dynamics and materials design for embedded phase-change memory
The Ge2Sb2Te5 alloy has served as the core material in phase-change memories with high switching speed and persistent storage capability at room temperature. However widely used, this composition is not suitable ...
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Article
Origins of structural and electronic transitions in disordered silicon
Structurally disordered materials pose fundamental questions1–4, including how different disordered phases (‘polyamorphs’) can coexist and transform from one phase to another5–9. Amorphous silicon has been extens...
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Open AccessA general-purpose machine-learning force field for bulk and nanostructured phosphorus
Elemental phosphorus is attracting growing interest across fundamental and applied fields of research. However, atomistic simulations of phosphorus have remained an outstanding challenge. Here, we show that a ...
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Article
Open AccessDe novo exploration and self-guided learning of potential-energy surfaces
Interatomic potential models based on machine learning (ML) are rapidly develo** as tools for material simulations. However, because of their flexibility, they require large fitting databases that are normal...
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Article
A stable compound of helium and sodium at high pressure
Helium is generally understood to be chemically inert and this is due to its extremely stable closed-shell electronic configuration, zero electron affinity and an unsurpassed ionization potential. It is not kn...
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Density-functional theory guided advances in phase-change materials and memories
Phase-change materials (PCMs) are promising candidates for novel data-storage and memory applications. They encode digital information by exploiting the optical and electronic contrast between amorphous and cr...
