Constraints on the location of the liquid–liquid critical point in water

Nature Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 3, 2025

Language: Английский

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Dissecting the Molecular Structure of the Air/Ice Interface from Quantum Simulations of the Sum-Frequency Generation Spectrum

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Ice interfaces are pivotal in mediating key chemical and physical processes such as heterogeneous reactions the environment, ice nucleation, cloud microphysics. At surface, water molecules form a quasi-liquid layer (QLL) with properties distinct from those of bulk. Despite numerous experimental theoretical studies, molecular-level understanding QLL has remained elusive. In this work, we use state-of-the-art quantum dynamics simulations realistic data-driven many-body potential to dissect vibrational sum-frequency generation (vSFG) spectrum air/ice interface terms contributions arising individual molecular layers, orientations, hydrogen-bonding topologies that determine properties. The agreement between simulated spectra provides picture evolution function temperature without need for empirical adjustments. emergence specific features vSFG suggests surface restructuring may occur at lower temperatures. This work not only underscores critical role interactions nuclear effects surfaces but also definitive QLL, which plays central multiphase relevance range fields, including atmospheric chemistry, cryopreservation, materials science.

Language: Английский

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MBX V1.2: Accelerating Data-Driven Many-Body Molecular Dynamics Simulations

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

The MBX software provides an advanced platform for molecular dynamics simulations, leveraging state-of-the-art MB-pol and MB-nrg data-driven many-body potential energy functions. Developed over the past decade, these functions integrate physics-based machine-learned terms trained on electronic structure data calculated at "gold standard" coupled-cluster level of theory. Recent advancements in have focused optimizing its performance, resulting release v1.2. While inherently nature ensures high accuracy, it poses computational challenges. v1.2 addresses challenges with significant performance improvements, including enhanced parallelism that fully harnesses power modern multicore CPUs. These enable simulations nanosecond time scales condensed-phase systems, significantly expanding scope high-accuracy, predictive complex systems powered by

Language: Английский

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Revealing the Water Structure at Neutral and Charged Graphene/Water Interfaces through Quantum Simulations of Sum Frequency Generation Spectra

ACS Nano, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

The structure and dynamics of water at charged graphene interfaces fundamentally influence molecular responses to electric fields with implications for applications in energy storage, catalysis, surface chemistry. Leveraging the realism MB-pol data-driven many-body potential advanced path-integral quantum dynamics, we analyze vibrational sum frequency generation (vSFG) spectrum graphene/water under varying charges. Our simulations reveal a distinctive dangling OH peak vSFG neutral graphene, consistent recent experimental findings yet markedly different from those earlier studies. As becomes positively charged, interfacial molecules reorient, decreasing intensity as groups turn away graphene. In contrast, orient their bonds toward negatively leading prominent corresponding spectrum. This charge-induced reorganization generates diverse range hydrogen-bonding topologies interface driven by variations underlying electrostatic interactions. Importantly, these structural changes extend into deeper layers, creating an unequal distribution pointing sheet. imbalance amplifies bulk spectral features, underscoring complexity interactions that shape interfaces.

Language: Английский

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Neural Network-Based Molecular Dynamics Simulation of Water Assisted by Active Learning

The Journal of Physical Chemistry B, Journal Year: 2025, Volume and Issue: unknown

Published: April 2, 2025

In our study, we combined classical molecular dynamics (MD) simulations with the simulated annealing (SA) method to explore conformational landscape of water molecules. By using K-means clustering method, processed MD simulation data extract representative samples structures used train a deep potential (DP) model. Our DeePMD showed accuracy in predicting structural properties compared DFT-MD results. Meanwhile, this approach achieves balanced prediction density and self-diffusion coefficients earlier simulations. These results highlight essential role sampling techniques training DP Furthermore, demonstrated effectiveness combining centroid (CMD) approach, which incorporates nuclear quantum effects (NQEs). This successfully reproduced shoulder feature at 3250 cm-1 Raman spectra for O-H stretch. Incorporating path integral into underscores importance considering NQEs understanding molecules' dynamic behaviors.

Language: Английский

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Electric Field’s Dueling Effects through Dehydration and Ion Separation in Driving NaCl Nucleation at Charged Nanoconfined Interfaces

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Investigating nucleation in charged nanoconfined environments under electric fields is crucial for many scientific and engineering applications. Here we study the of NaCl from aqueous solution near surfaces using machine-learning-augmented enhanced sampling molecular dynamics simulations. Our simulations successfully drive phase transitions between liquid solid phases NaCl. The stabilized fields, particularly at an intermediate surface charge density. We examine which physical characteristics solutions find that removal solvent water Cl- precursor plays a more critical role than accumulation ions. reveal competing effects on processes: they facilitate water, promoting nucleation, but also promote separation ion pairs, thereby hindering nucleation. This work provides framework studying processes insights design electrochemistry materials.

Language: Английский

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Structure making and breaking effects of ions on the anomalous diffusion of water revealed by machine learning potentials

Physical Chemistry Chemical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

The dynamics of water exhibits anomalous behavior in the solvation ions, and understanding perturbation that ions make on hydrogen bond structure remains an open question.

Language: Английский

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Scalable Super-hydrophobic Polyurethane/Fluorinated Polyurethane/SiO2 Nanofibrous Membranes for Waterproof and Breathable Application

Fibers and Polymers, Journal Year: 2025, Volume and Issue: unknown

Published: May 24, 2025

Language: Английский

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Short-Range Δ-Machine Learning: A Cost-Efficient Strategy to Transfer Chemical Accuracy to Condensed Phase Systems

Journal of Chemical Theory and Computation, Journal Year: 2025, Volume and Issue: unknown

Published: May 28, 2025

DFT-based machine-learning potentials (MLPs) are now routinely trained for condensed-phase systems, but surpassing DFT accuracy remains challenging due to the cost or unavailability of periodic reference calculations. Our previous work ( Phys. Rev. Lett. 2022, 129, 226001) demonstrated that high-accuracy MLPs can be within CCMD framework using extended yet finite Here, we introduce short-range Δ-Machine Learning (srΔML), a method starts from baseline MLP on low-level data and adds Δ-MLP correction based high-level cluster calculations at CC level. Applied liquid water, srΔML reduces required size (H2O)64 (H2O)15 significantly lowers number clusters needed, resulting in 50-200× reduction computational cost. The potential closely reproduces target accurately captures both two- three-body structural descriptors.

Language: Английский

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