The wetting of H2O by CO2

The Journal of Chemical Physics, Год журнала: 2024, Номер 161(8)

Опубликована: Авг. 28, 2024

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those the bulk. The CO2–H2O interface, in particular, has been subject studies on account its importance for carbon life cycle as well capture and sequestration schemes. Despite this attention, there remain open questions nature particularly concerning interfacial tension phase behavior CO2 at interface. In paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing benefits machine-learned potentials enhanced statistical sampling methods, present an initio-level description Interfacial tensions predicted 1 500 bars found be close agreement with experiment pressures which experimental data available. Structural analyses indicate buildup adsorbed, saturated film forming low pressure (20 bars) similar bulk liquid, preferential perpendicular alignment respect monolayer coincides reduced structuring water molecules This study highlights predictive macroscopic biphasic interfaces, mechanistic insight obtained into dioxide aggregation interface is high relevance geoscience, climate research, materials science.

Язык: Английский

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Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water

The Journal of Chemical Physics, Год журнала: 2024, Номер 161(20)

Опубликована: Ноя. 27, 2024

The introduction of machine learned potentials (MLPs) has greatly expanded the space available for studying Nuclear Quantum Effects computationally with ab initio path integral (PI) accuracy, MLPs' promise an accuracy comparable to that at a fraction cost. One challenges in development MLPs is need large and diverse training set calculated by methods. This dataset should ideally cover entire phase space, while not searching this using methods, as would be counterproductive generally intractable respect computational time. In paper, we present self-learning PI hybrid Monte Carlo Method mixed ML potential (SL-PIHMC-MIX), where allows study larger systems extension original SL-HMC method [Nagai et al., Phys. Rev. B 102, 041124 (2020)] methods systems. While generated can directly applied run long-time ML-PIMD simulations, demonstrate PIHMC-MIX trained exact reproduction structure obtained from PIMD. Specifically, find simulations require only 5000 evaluations 32-bead structure, compared 100 000 needed PIMD result.

Язык: Английский

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On the increase of the melting temperature of water confined in one-dimensional nano-cavities

The Journal of Chemical Physics, Год журнала: 2024, Номер 161(22)

Опубликована: Дек. 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.

Язык: Английский

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

Computational Materials Science, Год журнала: 2024, Номер 248, С. 113608 - 113608

Опубликована: Дек. 14, 2024

Язык: Английский

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