Structural, electronic, and dynamical properties of liquid water by <i>ab initio</i> molecular dynamics based on SCAN functional within the canonical ensemble

Zheng, Lixin; Chen, Mohan; Sun, Zhiwei; Ko, Hsin-Yu; Santra, Biswajit; Dhuvad, Pratikkumar; Wu, Xifan

doi:10.1063/1.5023611

Cited by 82 publications

(92 citation statements)

References 92 publications

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“…In sharp contrast, the less structured water modeled by SCAN functional largely facilitates the water rotation and gives rise to much more reasonable values of τ 1 =4.08(±0.02) ps and D R =0.24 ps −1 , respectively. The result consists with previous studies on diffusion coefficient 27 . As a conclusion, the modified libration peak from the SCAN functional indicates an improved description of the diffusion related dynamics in liquid water.…”

Section: Resultssupporting

confidence: 65%

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First-principles study of the infrared spectrum in liquid water from a systematically improved description of H-bond network

Chen

Zhang

et al. 2019

Phys. Rev. B

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Accurate ab initio theory of H-bond structure of liquid water requires high-level exchange correlation approximation from density functional theory. Based on the liquid structures modeled by ab initio molecular dynamics by using maximally-localized Wannier functions as basis, we study the infrared spectrum of water within the canonical ensemble. In particular, we employ both Perdew-Burke-Ernzerhof (PBE) functional within generalized gradient approximation (GGA) and the state-of-art meta-GGA level approximation provided by the strongly constrained and appropriately normed (SCAN) functional. We demonstrate that the SCAN functional improves not only the water structure but also the theoretical infrared spectrum of water. Our analyses show that the improvement in the stretching and bending bands can be mainly attributed to the better descriptions of directional H-bonding and the covalency at the inter-and intra-molecular levels, respectively. On the other hand, the better agreements in libration and hindered translation bands are due to the improved dynamics of the H-bond network enabled by less structured liquid towards the experimental direction. The predicted spectrum by SCAN shows a much better agreement with experimental data compared to the conventionally widely adopted PBE functional at the GGA level.

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Section: Resultssupporting

confidence: 65%

“…The above can be attributed to the improved prediction of covalency in water monomer. Indeed, the better agreement with experiments by SCAN functional in terms of bond angle, bong length, and binding energies in single water molecule and water clusters in gas phase have been widely recognized recently 26,27 .…”

Section: Resultsmentioning

confidence: 86%

First-principles study of the infrared spectrum in liquid water from a systematically improved description of H-bond network

Chen

Zhang

et al. 2019

Phys. Rev. B

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“…It resembles 47 the structure produced with the GGA functional of Becke 48 and Lee, Yang and Parr 49 (BLYP) with the Grimme dispersion correction (D2). 50 The origin of the discrepancy in comparison with previous SCAN studies 42,43 is likely due to the 30 K higher temperature used in these studies. This higher temperature was used to account for the effect of nuclear quantum effects.…”

Section: Structural Analysismentioning

confidence: 61%

“…20 To the best of our knowledge, this is the first demonstration that the SCAN functional significantly outperforms standard GGA functionals in reproducing bulk structural properties of ions in the condensed phase. As previously stated, earlier research has argued that SCAN can accurately reproduce bulk water structure 42,43 but this appears to require unphysically elevating the temperature by + 30 K. Similarly, the Na-Cl potential of mean force (PMF) in water has been computed with SCAN. 38 Different functionals may describe anion-water and cation-water interactions with signif-icantly different accuracy.…”

Section: Structural Analysismentioning

confidence: 99%

Quantifying the hydration structure of sodium and potassium ions: taking additional steps on Jacob’s Ladder

Duignan

Schenter²,

Fulton³

et al. 2019

Preprint

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<div><div><div><p>The ability to reproduce the experimental structure of water around the sodium and potassium ions is a key test of the quality of interaction potentials due to the central importance of these ions in a wide range of important phenomena. Here, we simulate the Na+ and K+ ions in bulk water using three density functional theory functionals: 1) The generalized gradient approximation (GGA) based dispersion corrected revised Perdew, Burke, and Ernzerhof functional (revPBE-D3) 2) The recently developed strongly constrained and appropriately normed (SCAN) functional 3) The random phase approximation (RPA) functional for potassium. We compare with experimental X-ray diffraction (XRD) and X-ray absorption fine structure (EXAFS) measurements to demonstrate that SCAN accurately reproduces key structural details of the hydra- tion structure around the sodium and potassium cations, whereas revPBE-D3 fails to do so. However, we show that SCAN provides a worse description of pure water in comparison with revPBE-D3. RPA also shows an improvement for K+, but slow convergence prevents rigorous comparison. Finally, we analyse cluster energetics to show SCAN and RPA have smaller fluctuations of the mean error of ion-water cluster binding energies compared with revPBE-D3.</p></div></div></div>

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“…However, applying an active learning strategy with a PBE reference method and a slightly larger 3.5 Å cut-off generates excellent agreement with the true AIMD simulation from ref. 63 , in only a few hours of total training time (Figure 2d-f). The real significance, of course, is in moving to more accurate ground-truth methods, for which a full MD would not be straightforward: indeed, using the same method, a hybrid DFT-quality water model can be generated within a few days, which would be inaccessible with other methods (the generation of the GAP model required ~5 days on 20 CPU cores, Figure 2g-i).…”

Section: Water Modelsmentioning

confidence: 99%

A Transferable Active-Learning Strategy for Reactive Molecular Force Fields

Young¹,

Johnston-Wood²,

Deringer³

et al. 2021

Preprint

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<p>Predictive simulations of dynamic processes in molecular systems require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct force-field models for large-scale molecular simulation by fitting to high-level quantum-mechanical data. However, machine-learned force fields generally require considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models for diverse chemical systems can be developed in an autonomous way, requiring only hundreds to a few thousand energy and gradient evaluations on the reference potential-energy surface. Our approach relies on a decomposition of the condensed-phase molecular system into intra- and inter-molecular terms, and on the definition of a prospective error metric to quantify accuracy. We demonstrate applications to a range of molecular systems: from bulk water, organic solvents, and a solvated ion onwards to the description of chemical reactivity, including, a bifurcating Diels–Alder reaction in the gas phase and non-equilibrium dynamics (S<sub>N</sub>2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for complex and/or reactive molecular systems. </p>

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Structural, electronic, and dynamical properties of liquid water by ab initio molecular dynamics based on SCAN functional within the canonical ensemble

Cited by 82 publications

References 92 publications

First-principles study of the infrared spectrum in liquid water from a systematically improved description of H-bond network

First-principles study of the infrared spectrum in liquid water from a systematically improved description of H-bond network

Quantifying the hydration structure of sodium and potassium ions: taking additional steps on Jacob’s Ladder

A Transferable Active-Learning Strategy for Reactive Molecular Force Fields

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