Dissecting the hydrogen bond network of water: Charge transfer and nuclear quantum effects

Science, Journal Year: 2024, Volume and Issue: 386(6726)

Published: Oct. 24, 2024

The molecular structure of water is dynamic, with intermolecular hydrogen (H) bond interactions being modified by both electronic charge transfer and nuclear quantum effects (NQEs). Electronic NQEs potentially change under acidic or basic conditions, but such details have not been measured. In this work, we developed correlated vibrational spectroscopy, a symmetry-based method that separates interacting from noninteracting molecules in self- cross-correlation spectra, giving access to previously inaccessible information. We found hydroxide (OH − ) donated ~8% more negative the H network water, hydronium (H 3 O + accepted ~4% less water. Deuterium oxide (D 2 O) had ~9% bonds compared O), solutions displayed dominant than ones.

Language: Английский

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ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials

Digital Discovery, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

ArcaNN is a comprehensive framework that employs concurrent learning to generate training datasets for reactive MLIPs in the condensed phase.

Language: Английский

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Revealing the molecular structures of α-Al2O3(0001)–water interface by machine learning based computational vibrational spectroscopy

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 161(12)

Published: Sept. 24, 2024

Solid–water interfaces are crucial to many physical and chemical processes extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establish clear correlations between specific spectral signatures distinct interfacial water structures, theoretical calculations molecular dynamics (MD) simulations required. These MD typically need relatively long trajectories (a few nanoseconds) achieve reliable SFG response function via the dipole moment–polarizability time correlation function. However, requirement for limits use of computationally expensive techniques, such as ab initio (AIMD) simulations, particularly complex solid–water interfaces. In this work, we present a pathway calculating vibrational spectra (IR, Raman, SFG) machine learning (ML)-accelerated methods. We employ both velocity–velocity approaches calculate spectra. Our results demonstrate successful acceleration AIMD calculation ML This advancement provides an opportunity complicated systems more rapidly at lower computational cost with aid ML.

Language: Английский

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Hydrogen bonding blues: Vibrational spectroscopy of the TIP3P water model

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(1)

Published: Jan. 2, 2025

The computational spectroscopy of water has proven to be a powerful tool for probing the structure and dynamics chemical systems providing atomistic insight into experimental vibrational spectroscopic results. However, such calculations have been limited biochemical due lack empirical frequency maps TIP3P model, which is used in many popular biomolecular force fields. Here, we develop an map model evaluate its efficacy reproducing water. We observe that calculated infrared Raman spectra are blueshifted narrowed compared spectra. Further analysis finds blueshift originates from shifted distribution frequencies, rather than other dynamical effects, suggesting forms significantly different electrostatic environment three-point models. This explored further by examining two-dimensional spectra, demonstrates significant first two transitions. Similarly, spectral diffusion timescales, evaluated through both center line slope frequency-frequency time correlation function demonstrate exhibits faster Finally, sum-frequency generation suggest despite these challenges, can provide phenomenological, qualitative, behavior at air-water lipid-water interfaces. As interfaces models hydrophobic hydrophilic environments observed systems, presently developed will useful future studies systems.

Language: Английский

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Why Proton Grotthuss Diffusion Slows down at the Air–Water Interface while Water Diffusion Accelerates

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: unknown, P. 2645 - 2653

Published: March 5, 2025

Excess proton diffusion at aqueous interfaces is crucial for applications including electrocatalysis, aerosol chemistry, and biological energy conversion. While have been proposed as pathways channeling protons, remains far less understood than in the bulk. Here we focus on air-water interface use density functional theory-based deep potential molecular dynamics simulations to reveal contrasting interface's impacts: excess slows down compared bulk, while water accelerates. This contrast stems from reduced hydrogen-bond coordination interface, which facilitates transient unstable rattling but impedes stable hops central Grotthuss diffusion. As a result, protons molecules diffuse comparable rates, stark departure bulk behavior. mechanistic insight delineates distinct limiting regimes bulk-enhanced interfacial diffusion, with important implications chemistry.

Language: Английский

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Applications of machine learning in surfaces and interfaces

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(1)

Published: March 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Language: Английский

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Aqueous solution chemistry in silico and the role of data-driven approaches

Chemical Physics Reviews, Journal Year: 2024, Volume and Issue: 5(2)

Published: June 1, 2024

The use of computer simulations to study the properties aqueous systems is, today more than ever, an active area research. In this context, during last decade there has been a tremendous growth in data-driven approaches develop accurate potentials for water as well characterize its complexity chemical and biological contexts. We highlight progress, giving historical on path development many-body reactive model chemistry, including role machine learning strategies. focus specifically conceptual methodological challenges along way performing that seek tackle problems modeling chemistry solutions. conclusion, we summarize our perspectives integration advanced data-science techniques provide insights into physical how will influence future.

Language: Английский

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Impact of interfacial curvature on molecular properties of aqueous interfaces

The Journal of Chemical Physics, Journal Year: 2024, Volume and Issue: 160(23)

Published: June 18, 2024

The curvature of soft interfaces plays a crucial role in determining their mechanical and thermodynamic properties, both at macroscopic microscopic scales. In the case air/water interfaces, particular attention has recently focused on water microdroplets, due to distinctive chemical reactivity. However, specific impact molecular properties interfacial reactivity so far remained elusive. Here, we use dynamics simulations determine effect broad range structural, dynamical, thermodynamical interface. For droplet, flat interface, cavity, successively examine structure hydrogen-bond network its relation vibrational spectroscopy, translation, rotation, exchanges, thermodynamics ion solvation ion-pair dissociation. Our show that predominantly impacts through fraction dangling OH groups molecules. contrast, limited dissociation thermodynamics. this suggests alone cannot fully account for measured these systems, which are great importance catalysis atmospheric chemistry.

Language: Английский

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Neural network potentials for exploring condensed phase chemical reactivity

Comptes Rendus Chimie, Journal Year: 2024, Volume and Issue: 27(S5), P. 1 - 17

Published: June 18, 2024

Language: Английский

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Unveiling the Role of Solvent in Solution Phase Chemical Reactions using Deep Potential-Based Enhanced Sampling Simulations

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: unknown, P. 9932 - 9938

Published: Sept. 23, 2024

We have used a deep learning-based active learning strategy to develop

Language: Английский

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