On the increase of the melting temperature of water confined in one-dimensional nano-cavities DOI
Flaviano Della Pia, Andrea Zen, Venkat Kapil

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(22)

Published: Dec. 10, 2024

Water confined in nanoscale cavities plays a crucial role everyday phenomena geology and biology, as well technological applications at the water–energy nexus. However, even understanding basic properties of nano-confined water is extremely challenging for theory, simulations, experiments. In particular, determining melting temperature quasi-one-dimensional ice polymorphs carbon nanotubes has proven to be an exceptionally difficult task, with previous experimental classical simulation approaches reporting values ranging from ∼180 K up ∼450 ambient pressure. this work, we use machine learning potential that delivers first principles accuracy (trained density functional theory approximation revPBE0-D3) study phase diagram confinement diameters 9.5 < d 12.5 Å. We find several distinct melt surprisingly narrow range between ∼280 ∼310 K, mechanism depends on nanotube diameter. These results shed new light one-dimension have implications operating conditions carbon-based filtration desalination devices.

Language: Английский

Grand-Canonical First Principles-Based Calculations of Electrochemical Reactions DOI Creative Commons
Ryosuke Jinnouchi

Journal of The Electrochemical Society, Journal Year: 2024, Volume and Issue: 171(9), P. 096502 - 096502

Published: Aug. 23, 2024

This article introduces the first principles-based grand-canonical formalisms of several representative electronic structure calculation methods in electrochemistry, which are essential for elucidating atomic-scale mechanisms electrochemical reactions and discovering guiding principles designing advanced materials. While most applications still rely on approximate structures obtained by static calculations at absolute zero, foundational theories more rigorous molecular dynamics simulations also developing. I discuss that combine these with emerging machine-learning interatomic potentials, suggesting this approach could pave way to predict thermodynamics kinetics finite temperatures purely from principles.

Language: Английский

Citations

1
Machine learning interatomic potential for friction study in silicon and molybdenum disulfide DOI

Shujia Wan,

Ruiting Tong, Bing Han

et al.

Computational Materials Science, Journal Year: 2024, Volume and Issue: 248, P. 113608 - 113608

Published: Dec. 14, 2024

Language: Английский

Citations

0
On the increase of the melting temperature of water confined in one-dimensional nano-cavities DOI
Flaviano Della Pia, Andrea Zen, Venkat Kapil

et al.

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(22)

Published: Dec. 10, 2024

Water confined in nanoscale cavities plays a crucial role everyday phenomena geology and biology, as well technological applications at the water–energy nexus. However, even understanding basic properties of nano-confined water is extremely challenging for theory, simulations, experiments. In particular, determining melting temperature quasi-one-dimensional ice polymorphs carbon nanotubes has proven to be an exceptionally difficult task, with previous experimental classical simulation approaches reporting values ranging from ∼180 K up ∼450 ambient pressure. this work, we use machine learning potential that delivers first principles accuracy (trained density functional theory approximation revPBE0-D3) study phase diagram confinement diameters 9.5 < d 12.5 Å. We find several distinct melt surprisingly narrow range between ∼280 ∼310 K, mechanism depends on nanotube diameter. These results shed new light one-dimension have implications operating conditions carbon-based filtration desalination devices.

Language: Английский

Citations

0