Random Sampling Versus Active Learning Algorithms for Machine Learning Potentials of Quantum Liquid Water

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

Published: Jan. 14, 2025

Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of system interest. As construction this is computationally demanding, many schemes for identifying most important structures have been proposed. Here, we compare performance high-dimensional neural network (HDNNPs) quantum liquid water at ambient conditions trained to sets constructed using random sampling as well various flavors active based on query by committee. Contrary common understanding learning, find that a given set size, leads smaller test errors not included in training process. In our analysis, show can be related small energy offsets caused bias added which overcome instead correlations an error measure invariant such shifts. Still, all HDNNPs yield very similar and structural properties water, demonstrates robustness procedure with respect algorithm even when few 200 structures. However, preliminary potentials, reasonable initial avoid unnecessary extension covered configuration less relevant regions.

Language: Английский

---

PW-SMD: A Plane-Wave Implicit Solvation Model Based on Electron Density for Surface Chemistry and Crystalline Systems in Aqueous Solution

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(15), P. 6826 - 6847

Published: July 18, 2024

Electron density-based implicit solvation models are a class of techniques for quantifying effects and calculating free energies without an explicit representation solvent molecules. Integral to the accuracy modeling is proper definition shell separating solute molecule from environment, allowing physical partitioning solvation. Unlike state-of-the-art molecular quantum chemistry calculations,

Language: Английский

---

Chemically accurate predictions for water adsorption on Brønsted sites of zeolite H-MFI

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(36), P. 23588 - 23599

Published: Jan. 1, 2024

Accurate predictions of the heat water adsorption and protonation state requires passing from density functional theory (PBE+D) to wavefunction methods (MP2).

Language: Английский

---

Crumbling Crystals: On the Dissolution Mechanism of NaCl in Water

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(42), P. 26933 - 26942

Published: Jan. 1, 2024

Machine-learned atomistic simulations reveal that NaCl dissolves via a crumbling mechanism.

Language: Английский

---

Atomistic simulation of batteries via machine learning force fields: from bulk to interface

Journal of Energy Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

---

Structural and dynamical properties of aqueous NaCl brines confined in kaolinite nanopores

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(12)

Published: March 24, 2025

Quantifying thermodynamics, structural, and dynamical properties of brine confined in clay pores is critical for a variety geo-energy applications, including underground hydrogen storage (UHS) carbon capture sequestration (CCS). Atomistic molecular dynamics simulations are applied here to study aqueous NaCl brines within 10-Å kaolinite slit pores. concentrations chosen at 5, 10, 12.5, 15 wt. %, all below the solubility limit high enough provide statistically relevant information. The distribution ions nanopores found not be homogeneous. Explicitly, Na+ cations, preferentially attracted siloxane surface, accumulate regions with low water density, whereas Cl− anions, gibbsite surface kaolinite, hydration layers. Confinement affects ions, ion pairing being more pronounced pore than bulk solutions similar temperatures, pressures, compositions. Conversely, affect water. For example, lifetime water–water bonds confinement shortened shells; increasing salinity from 5 12.5 % reduces likelihood density fluctuations near surfaces, although when concentration rises anions enhance layer surface. simulated trajectories studied further extract diffusion coefficients. While nanopore mobility species, non-monotonic trends observed as function salt concentration. seem associated pairing. Furthermore, coefficients cations predicted higher those which contrary what typically brines. Because correlated such gases water, our observations may have important implications applications UHS CCS.

Language: Английский

---

On the Physical Origins of Reduced Ionic Conductivity in Nanoconfined Electrolytes

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

Published: March 25, 2025

Ion transport through nanoscale pores is at the heart of numerous energy storage and separation technologies. Despite significant efforts to uncover complex interplay ion–ion, ion–water, ion–pore interactions that give rise these processes, atomistic mechanisms ion motion in confined electrolytes remain poorly understood. In this work, we use machine learning-based molecular dynamics simulations characterize with first-principles-level accuracy aqueous NaCl graphene slit pores. We find ionic conductivity decreases as degree confinement increases, a trend governed by changes both self-diffusion dynamic ion–ion correlations. show coefficients our ions are strongly influenced overall electrolyte density, which nonmonotonically height based on layering water molecules within pore. further observe shift ions' diffusion mechanism toward more vehicular increases. ubiquity ideal solution (Nernst–Einstein) assumptions field, nonideal contributions become pronounced under confinement. This increase correlations arises not simply from an fraction associated ions, commonly assumed, but pair lifetimes. By building mechanistic understanding transport, work provides insights could guide design nanoporous materials optimized for efficient selective transport.

Language: Английский

---

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

---

Ab Initio Simulation of Liquid Water without Artificial High Temperature

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

Comprehending the structure and dynamics of water is crucial in various fields, such as desalination, ion separation, electrocatalysis, biochemical processes. While reported works show that

Language: Английский

---

Modelling ligand exchange in metal complexes with machine learning potentials

Faraday Discussions, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural dynamic properties diverse chemical environments remains challenging for both force fields

Language: Английский

---