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Open AccessBeyond potentials: Integrated machine learning models for materials
Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from ...
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Open AccessThermodynamics and dielectric response of BaTiO3 by data-driven modeling
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort, requiring an accurate description of the electronic proces...
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Reply to: On the liquid–liquid phase transition of dense hydrogen
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Gas-sieving zeolitic membranes fabricated by condensation of precursor nanosheets
The synthesis of molecular-sieving zeolitic membranes by the assembly of building blocks, avoiding the hydrothermal treatment, is highly desired to improve reproducibility and scalability. Here we report exfol...
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Open AccessAuthor Correction: Simulating the ghost: quantum dynamics of the solvated electron
A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21706-2
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Open AccessSimulating the ghost: quantum dynamics of the solvated electron
The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achie...
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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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Evidence for supercritical behaviour of high-pressure liquid hydrogen
Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behaviour upon compression1. Since Wigner predicted the dissociation and metallization of solid hydrogen at megabar ...
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Reference Work Entry In depth
Machine Learning of Atomic-Scale Properties Based on Physical Principles
We briefly summarize the kernel regression approach, as used recently in materials modeling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be ...
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Chapter
Machine-Learning of Atomic-Scale Properties Based on Physical Principles
We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be...
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Article
Open AccessQuantum mechanical static dipole polarizabilities in the QM7b and AlphaML showcase databases
While density functional theory (DFT) is often an accurate and efficient methodology for evaluating molecular properties such as energies and multipole moments, this approach often yields larger errors for res...
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Article
Open AccessChemical shifts in molecular solids by machine learning
Due to their strong dependence on local atonic environments, NMR chemical shifts are among the most powerful tools for strucutre elucidation of powdered solids or amorphous materials. Unfortunately, using them...
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Article
Open AccessMap** uncharted territory in ice from zeolite networks to ice structures
Ice is one of the most extensively studied condensed matter systems. Yet, both experimentally and theoretically several new phases have been discovered over the last years. Here we report a large-scale density...
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Article
Nuclear quantum effects enter the mainstream
Atomistic simulations of chemical, biological and materials systems have become increasingly precise and predictive owing to the development of accurate and efficient techniques that describe the quantum mecha...
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Living Reference Work Entry In depth
Machine Learning of Atomic-Scale Properties Based on Physical Principles
We briefly summarize the kernel regression approach, as used recently in materials modeling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be ...
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Article
Open AccessMap** and classifying molecules from a high-throughput structural database
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and p...
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Article
Open AccessThermally-nucleated self-assembly of water and alcohol into stable structures at hydrophobic interfaces
At the interface with solids, the mobility of liquid molecules tends to be reduced compared with bulk, often resulting in increased local order due to interactions with the surface of the solid. At room temper...
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Machines learn to recognize glasses
The dynamics of a viscous liquid undergo a dramatic slowdown when it is cooled to form a solid glass. Recognizing the structural changes across such a transition remains a major challenge. Machine-learning met...
