Pith. sign in

REVIEW 2 major objections 4 minor 57 references

Machine-Learning-Accelerated Surface Exploration of Reconstructed BiVO$_{4}$(010) and Characterization of Their Aqueous Interfaces

T0 review · 2 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper argues that reconstructed Bi-rich BiVO4(010) surfaces spontaneously dissociate water, with bare under-coordinated Bi sites as the active centers.

desk verdict Valuable MLIP-driven surface structure search and Pourbaix analysis; the water dissociation claim is plausible but the quantified percentages rest on one short, un-replicated MD run per surface. read the letter →

arxiv 2412.08126 v1 pith:K3YSTM57 submitted 2024-12-11 cond-mat.mtrl-sci physics.chem-ph

Yonghyuk Lee , Taehun Lee This is my paper · ORCID
classification cond-mat.mtrl-sciphysics.chem-ph PACS 68.08.-p71.15.Mb82.65.+r
keywords BiVO4surfacereconstructionmachine-learninginteratomicpotentialactivelearningsimulatedannealingPourbaixdiagramabinitiomoleculardynamicswaterdissociation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper aims to show that the surface that actually does the work in a bismuth vanadate photoanode is not the clean, bulk-truncated one that most models assume. Using a machine-learned interatomic potential driven by active learning and simulated annealing, the authors searched the surface reconstruction space of BiVO4(010) and found 494 distinct structures, then used hybrid-functional thermodynamics to pick the ones stable under photoelectrochemical conditions. When they ran hybrid-functional molecular dynamics with explicit water on those reconstructed surfaces, they observed spontaneous water dissociation on the Bi-rich and V-rich surfaces—up to 75 percent of surface metal atoms carrying dissociated water on Bi-rich models—while the stoichiometric surface stayed intact. The paper argues that bare, under-coordinated bismuth sites, which only exist in the reconstructed surfaces, are the active centers for this dissociation. If true, this reframes how the aqueous BiVO4 interface should be modeled and suggests that surface reconstruction is essential for surface hydration and reactivity.

What carries the argument

The load-bearing element is a multi-stage computational workflow rather than a single identity. First, an active-learning-trained Gaussian Approximation Potential (a machine-learned surrogate of the density-functional potential-energy surface) drives simulated-annealing searches that rearrange surface and near-surface atoms, producing 494 unique reconstructed BiVO4(010) structures across Bi-rich, V-rich, and mixed stoichiometries. Second, a two-step screening with PBE and a dielectric-dependent PBE0 hybrid functional builds a surface Pourbaix diagram that selects electrochemically stable structures under Bi- and V-rich electrolyte conditions. Finally, Born-Oppenheimer molecular dynamics with the same hybrid functional and explicit water at 350 K for 7.5 ps probes the aqueous interface. The mechanistic heart is the bare, under-coordinated Bi site: water binds there and dissociates via an indirect pathway through a transient hydronium ion or a direct proton transfer to a bridging oxygen, forming surface hydroxyls.

What would settle it

Repeating the hybrid-functional molecular dynamics on the t-BiO2 surface with several independent water starting configurations and trajectories longer than 20 picoseconds would test the claim: if the dissociation fraction falls to the near-zero level of the stoichiometric surface, the spontaneous-dissociation mechanism would be falsified. On the experimental side, operando surface-specific vibrational spectroscopy on a Bi-rich BiVO4(010) electrode under anodic conditions should detect surface hydroxyl signals if the predicted dissociation occurs.

Watch

Extended reading notes

Core claim

The paper's central claim is that reconstructed Bi-rich BiVO4(010) surfaces, and to a lesser extent V-rich ones, spontaneously dissociate water at the aqueous interface, whereas the stoichiometric surface does not. The authors report this as the first theoretical observation of spontaneous water dissociation on BiVO4. On the t-BiO2 and t-BiO3 surfaces, water adsorbed at a bare, under-coordinated Bi site transfers a proton to a neighboring water molecule or directly to a bridging oxygen, yielding surface hydroxyls; the maximum dissociation percentage reaches about 75 percent on Bi-rich surfaces. This behavior is attributed to the low-coordinated Bi sites exposed only by reconstruction, which act as the active centers for dissociation, and it is presented as a mechanistic explanation for the enhanced hydration of experimentally observed Bi-rich surfaces under photoelectrochemical anodic conditions.

Load-bearing premise

The entire mechanistic conclusion rests on the assumption that a single 7.5-picosecond simulation, started from a water configuration built for the stoichiometric surface and run at 350 K, represents the equilibrium aqueous interface on each reconstructed surface.

Editorial extensions

If this is right

  • Experimentally observed Bi-rich BiVO4 surfaces under photoelectrochemical anodic conditions likely correspond to the t-BiO2- and t-BiO3-type reconstructions identified here, which are far more hydrated than the stoichiometric surface.
  • Surface reconstruction, not the bulk-truncated termination, should be the starting model for aqueous BiVO4 interfaces in studies of stability and reactivity.
  • Bare, low-coordinated Bi sites act as the active centers for water dissociation; V sites play a secondary role on reconstructed surfaces.
  • The active-learning-plus-Pourbaix workflow can be transferred to other multicomponent oxides to predict stable reconstructed surfaces and their interfacial reactivity.
  • The predicted surface structures offer concrete targets for experimental validation with operando or ex situ surface characterization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The dissociation percentages come from single 7.5 ps trajectories initialized from one water configuration; longer runs or multiple starting configurations could shift quantitative fractions, though the qualitative contrast between reconstructed and stoichiometric surfaces may persist.
  • If the bare-Bi mechanism holds, then the anodic vanadium dissolution that creates Bi-rich overlayers would actually enhance water activation, implying that photoelectrochemical activity and surface stability are dynamically coupled rather than fixed properties.
  • The short O-O motifs observed on oxidized reconstructed surfaces resemble dioxygen-like species; connecting these to oxygen-evolution-reaction intermediates would be a natural next step, but the paper does not make that claim.
  • An applied-electrode-potential treatment (for example, grand canonical molecular dynamics) could test whether the dissociation behavior persists under explicit bias, which the present simulations at the water interface do not include.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper presents a computational workflow for exploring reconstructed BiVO4(010) surfaces and their aqueous interfaces. A Gaussian Approximation Potential (GAP) MLIP, iteratively trained via active learning, is combined with simulated annealing in p(1x1) cells to generate 494 unique surface configurations across 13 stoichiometries. These are screened with PBE and PBE0 (alpha=0.22) to construct a surface Pourbaix diagram, identifying Bi-rich (t-BiO2, t-BiO3) and V-rich (t-VO2, t-VO4) structures as electrochemically stable under relevant conditions. Born-Oppenheimer PBE0-MD simulations (7.5 ps, 350 K) of five aqueous interfaces show no dissociation on the stoichiometric surface but significant water dissociation on the reconstructed surfaces, with the largest fractions attributed to bare, under-coordinated Bi atoms on t-BiO2/t-BiO3. The authors claim this is the first theoretical observation of spontaneous water dissociation on BiVO4(010).

Significance. If the dissociation result is robust, the paper makes a valuable contribution: it proposes concrete structural models for experimentally observed Bi-rich BiVO4 surfaces, identifies a possible role for under-coordinated Bi sites in surface hydration, and demonstrates an active-learning MLIP workflow for multicomponent oxide surfaces with modest DFT cost (355 DFT calculations). The workflow is described in enough detail to be reproduced, and the training data are promised on Materials Cloud. However, the central dissociation claim currently rests on short, unreplicated MD simulations with a non-equilibrated initial water configuration, so the quantitative conclusions and the mechanistic attribution require additional validation.

major comments (2)
  1. [Aqueous BiVO4(010) Interfaces and their Dynamical Properties; Methods (DFT Calculations)] The central claim of significant spontaneous water dissociation on reconstructed Bi-rich surfaces rests on a single 7.5 ps PBE0-MD trajectory per surface, with the initial water configuration taken from a previous stoichiometric BiVO4(010)-water study (Ref. 39) and not re-equilibrated for each reconstructed surface. Figure 3a shows the dissociation fraction still rising or fluctuating at the end of the 7.5 ps runs, so the reported 'maximum dissociation percentage of 75%' is a transient maximum rather than a converged steady-state value. With no independent replicates or sensitivity tests to the initial water arrangement, the mechanistic attribution of dissociation to bare, under-coordinated Bi sites is not yet quantitatively established. The authors should provide longer trajectories, multiple starting water configurations, and time-averaged dissociation fractions with error estimates.
  2. [Global Optimization for Reconstructed BiVO4(010) Sampling] The structure search is confined to the p(1x1) surface unit cell; the 494 unique structures are therefore only unique within this cell. As reconstructed surfaces with longer periodicity are common in multicomponent oxides, the claim that the workflow 'identified 494 unique reconstructed surface structures' should be explicitly qualified as p(1x1)-limited. The later p(2x2) re-optimization of selected structures is a useful check, but it does not recover structures that would only emerge from a genuinely p(2x2) or larger-cell global search. This limitation should be stated in the abstract and conclusions.
minor comments (4)
  1. [Methods (DFT Calculations)] The number of distinct aqueous interfaces is inconsistent: the Methods states 'five different aqueous BiVO4(010)-water interfaces', while the Results discusses six (t-BiVO4, t-BiVO6, t-BiO2, t-BiO3, t-VO2, t-VO4). Please clarify.
  2. [Results and Discussion, Aqueous BiVO4(010) Interfaces and their Dynamical Properties] The phrase 'spontaneous water dissociation, at the early stages of the PBE0-MD simulations' contains an awkward comma after 'dissociation'; more importantly, 'early stages' should be reconciled with the claim that the observed dissociation is an equilibrium property.
  3. [Figure 3 caption] The caption does not define the 'percentage of dissociated water molecules' (it is defined in the text as the number of dissociated water molecules divided by the total number of outermost metal atoms) nor the meaning of 'spikes' relative to 'steps'. Adding this information to the caption would improve readability.
  4. [Abstract and Introduction] The claim that this is 'the first theoretical report' of spontaneous water dissociation on BiVO4(010) should be supported by a more thorough literature search or softened to avoid an unsupported novelty assertion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the surface reconstructions and water dissociation results are produced by the MLIP/DFT/PBE0-MD workflow rather than imposed by its inputs.

full rationale

The paper's derivation chain is not circular in the sense defined here. The MLIP is trained on DFT data and used only to propose candidate reconstructions; all reported surface structures are re-optimized with GGA-PBE and dielectric-dependent PBE0 (Methods: 'After exploring the unique surfaces using GAP, we re-optimized the obtained BiVO4 surfaces using DFT...'), so the 494 unique structures are not fitted outputs. The surface Pourbaix diagram is built from DFT total energies and experimental ionic formation energies, and the six selected surfaces are those stable under stated pH/potential conditions. The central claim, spontaneous water dissociation on reconstructed Bi- and V-rich surfaces, is a direct observable of the PBE0 Born-Oppenheimer MD trajectories (Figure 3), not a parameter fitted to that outcome. The stoichiometric surface is simulated with the same protocol and shows no dissociation, providing an internal control. The active-learning protocol is cited from Refs. 19 and 20, which include one of the present authors, but this citation supplies the general methodology and is not used to justify the physical conclusions; the paper also reports its own training convergence and force-error checks in the SI. The use of a single 7.5 ps trajectory per surface and of an initial water configuration taken from a prior stoichiometric-interface study (Ref. 39) is a legitimate robustness and sampling concern, but it does not make the prediction equivalent to the input by construction. No equation or definition in the paper identifies a predicted quantity with a fitted parameter or with the target claim, so no circular step meets the evidentiary bar.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claims rest on a chain of modeling assumptions: the tetragonal-phase proxy, the Pourbaix thermodynamic framework with experimental ion energies, the restricted p(1x1) search, and the short single-trajectory AIMD. No new physical entities are introduced. The free parameters are mostly adopted from prior literature or chosen for convenience, not fitted to the target result.

free parameters (5)
  • PBE0 exact-exchange fraction alpha = 0.22 = 1/epsilon_inf
    Adopted from prior BiVO4 computational setups (Refs. 32,33); sets the amount of exact exchange in all PBE0 static and MD calculations, affecting surface energetics and water dissociation barriers.
  • GAP MLIP hyperparameters (2B and SOAP) = see Table S1
    Hand-chosen during fitting; control the surrogate PES used in simulated annealing and active learning. Specific values are not in the main text.
  • Electrolyte ion concentrations for Pourbaix diagram = 10^-6 M Bi3+/V2+
    Chosen to represent Bi- and V-rich conditions; shifts the equilibria in the surface Pourbaix diagram and thus which reconstructed surfaces are predicted stable.
  • Photovoltage shift for PEC mapping = 1.7 V
    Taken from Ref. 54 to convert EC onset to PEC onset; the conclusion that t-BiO2 and t-BiO3 are observable under photocharging depends on this value.
  • AIMD temperature = 350 K
    Set to ensure water diffusion; above room temperature, so 'spontaneous' dissociation at 350 K may not reflect behavior at 300 K.
assumptions (6)
  • domain assumption Tetragonal BiVO4 (I41/a) serves as a valid structural proxy for monoclinic BiVO4 (C2/c) (010) surface.
    Used throughout; the paper argues similarity of band structures and symmetry of the (001) face of the tetragonal phase to the (010) of monoclinic. Surface reconstructions and water chemistry are computed for this proxy.
  • domain assumption Surface Pourbaix thermodynamics with solvated ion reservoirs (Bi3+, V2+) and experimental formation energies correctly captures electrochemical stability.
    Standard ab initio thermodynamics approach, but depends on experimental free energies of aqueous ions and assumed equilibrium with 10^-6 M electrolytes.
  • ad hoc to paper Simulated annealing with a harmonic repulsive wall and lateral mass transfer in a p(1x1) cell explores the relevant reconstruction landscape.
    The wall prevents desorption but could bias structures; exploration is limited to p(1x1) cells, so larger reconstructions are unseen.
  • ad hoc to paper The initial water configuration for AIMD (taken from a previous stoichiometric BiVO4-water study) is appropriate for reconstructed surfaces.
    The same water box is placed on different reconstructed surfaces; this could bias adsorption and dissociation events.
  • domain assumption 7.5 ps of PBE0 MD at 350 K is sufficient to observe and quantify spontaneous water dissociation.
    Single trajectory per surface; no convergence checks or multiple seeds; dissociation percentages are based on this short window.
  • ad hoc to paper The active-learning termination criteria (similarity threshold kappa_crit, force error) ensure completeness of structure exploration.
    The search stops when no new unknown structures or force error below threshold; this does not guarantee all relevant reconstructions found.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Machine-Learning-Accelerated Surface Exploration of Reconstructed BiVO$_{4}$(010) and Characterization of Their Aqueous Interfaces." pith.science (2026). https://pith.science/paper/K3YSTM57

@misc{pith2026241208126,
  author       = {Pith},
  title        = {Pith review of: Machine-Learning-Accelerated Surface Exploration of Reconstructed BiVO$_4$(010) and Characterization of Their Aqueous Interfaces},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K3YSTM57}},
  note         = {Machine review of arXiv:2412.08126}
}
abstract

Understanding the semiconductor-electrolyte interface in photoelectrochemical (PEC) systems is crucial for optimizing stability and reactivity. Despite the challenges in establishing reliable surface structure models during PEC cycles, this study explores the complex surface reconstructions of BiVO$_{4}$(010) by employing a computational workflow integrated with a state-of-the-art active learning protocol for a machine-learning interatomic potential and global optimization techniques. Within this workflow, we identified 494 unique reconstructed surface structures that surpass conventional chemical intuition-driven, bulk-truncated models. After constructing the surface Pourbaix diagram under Bi- and V-rich electrolyte conditions using density functional theory and hybrid functional calculations, we proposed structural models for the experimentally observed Bi-rich BiVO$_{4}$ surfaces. By performing hybrid functional molecular dynamics simulations with explicit treatment of water molecules on selected reconstructed BiVO$_{4}$(010) surfaces, we observed spontaneous water dissociation, marking the first theoretical report of this phenomenon. Our findings demonstrate significant water dissociation on reconstructed Bi-rich surfaces, highlighting the critical role of bare and under-coordinated Bi sites (only observable in reconstructed surfaces) in driving hydration processes. Our work establishes a foundation for understanding the role of complex, reconstructed Bi surfaces in surface hydration and reactivity. Additionally, our theoretical framework for exploring surface structures and predicting reactivity in multicomponent oxides offers a precise approach to describing complex surface and interface processes in PEC systems.

Figures

Figures reproduced from arXiv: 2412.08126 by the authors.

Figure 1
Figure 1. Computational workflow for structure exploration, stability evaluations of re [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. (a) PBE0 surface Pourbaix diagrams for different BiVO [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Water dissociation on reconstructed BiVO [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: (a) Radial distribution functions g(r) for selected surface atoms on the t-BiVO4 and t-BiO2 surfaces. (b) Fraction of surface MOn polyhedra and percentage of ∗OH and ∗H2O surface adsorbates amongst oxygen species bonded with the outermost Bi and V atoms. Schematic illu…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

57 extracted references · 56 canonical work pages

  1. [1]

    I.; Lana-Villarreal, T.; G \'o mez, R

    Monllor-Satoca, D.; D \' ez-Garc \' a, M. I.; Lana-Villarreal, T.; G \'o mez, R. Photoelectrocatalytic Production of Solar Fuels with Semiconductor Oxides: Materials, Activity and Modeling . Chem. Commun. 2020, 56, 12272--12289

    work page 2020

  2. [2]

    A.; Collado, L.; Villar-Garc \' a, I

    Barawi, M.; Mesa, C. A.; Collado, L.; Villar-Garc \' a, I. J.; Oropeza, F.; de la Pe\ n a O'Shea , V. A.; Garc \' a-Tecedor, M. Latest Advances in In - Situ and Operando X-Ray-Based Techniques for the Characterisation of Photoelectrocatalytic Systems . J. Mater. Chem. A 2024,

    work page 2024

  3. [3]

    Venugopal, A.; Kas, R.; Hau, K.; Smith, W. A. Operando Infrared Spectroscopy Reveals the Dynamic Nature of Semiconductor--Electrolyte Interface in Multinary Metal Oxide Photoelectrodes . J. Am. Chem. Soc. 2021, 143, 18581--18591

    work page 2021

  4. [4]

    The Impact of Surface Composition on the Interfacial Energetics and Photoelectrochemical Properties of BiVO _4

    Lee, D.; Wang, W.; Zhou, C.; Tong, X.; Liu, M.; Galli, G.; Choi, K.-S. The Impact of Surface Composition on the Interfacial Energetics and Photoelectrochemical Properties of BiVO _4 . Nat. Energy 2021, 6, 287--294

    work page 2021

  5. [5]

    Ab Initio Thermodynamics and First-Principles Microkinetics for Surface Catalysis

    Reuter, K. Ab Initio Thermodynamics and First-Principles Microkinetics for Surface Catalysis. Catal. Lett. 2016, 146, 541--563

    work page 2016

  6. [6]

    The Rise of Ab Initio Surface Thermodynamics

    Lee, T.; Soon, A. The Rise of Ab Initio Surface Thermodynamics . Nat. Catal. 2024, 7, 4--6

    work page 2024

  7. [7]

    Complex Surface Reconstructions Solved by Ab Initio Molecular Dynamics

    Kresse, G.; Bergermayer, W.; Podloucky, R.; Lundgren, E.; Koller, R.; Schmid, M.; Varga, P. Complex Surface Reconstructions Solved by Ab Initio Molecular Dynamics . Appl. Phys. A 2003, 76, 701--710

    work page 2003

  8. [8]

    B.; Hammer, B

    Vilhelmsen, L. B.; Hammer, B. A Genetic Algorithm for First Principles Global Structure Optimization of Supported Nano Structures . J. Chem. Phys. 2014, 141, 044711

    work page 2014

Show all 57 references
  1. [9]

    Panosetti, C.; Krautgasser, K.; Palagin, D.; Reuter, K.; Maurer, R. J. Global Materials Structure Search with Chemically Motivated Coordinates . Nano Lett. 2015, 15, 8044--8048

    2015

  2. [10]

    B.; Qiu, T.; Rappe, A

    Wexler, R. B.; Qiu, T.; Rappe, A. M. Automatic Prediction of Surface Phase Diagrams Using Ab Initio Grand Canonical Monte Carlo . J. Phys. Chem. C 2019, 123, 2321--2328

    2019

  3. [11]

    Zhou, Y.; Scheffler, M.; Ghiringhelli, L. M. Determining Surface Phase Diagrams Including Anharmonic Effects . Phys. Rev. B 2019, 100, 174106

    2019

  4. [12]

    Zhang, Z.; Wei, Z.; Sautet, P.; Alexandrova, A. N. Hydrogen-Induced Restructuring of a Cu (100) Electrode in Electroreduction Conditions . J. Am. Chem. Soc. 2022, 144, 19284--19293

    2022

  5. [13]

    L.; Caro, M

    Deringer, V. L.; Caro, M. A.; Cs \'a nyi, G. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science . Adv. Mater. 2019, 31, 1902765

    2019

  6. [14]

    J.; Musil, F.; Ceriotti, M

    Willatt, M. J.; Musil, F.; Ceriotti, M. Atom-Density Representations for Machine Learning . J. Chem. Phys. 2019, 150, 154110

    2019

  7. [15]

    V.; Thompson, A

    Zuo, Y.; Chen, C.; Li, X.; Deng, Z.; Chen, Y.; Behler, J.; Cs \'a nyi, G.; Shapeev, A. V.; Thompson, A. P.; Wood, M. A.; Ong, S. P. Performance and Cost Assessment of Machine Learning Interatomic Potentials . J. Phys. Chem. A 2020, 124, 731--745

    2020

  8. [16]

    L.; Bart \'o k, A

    Deringer, V. L.; Bart \'o k, A. P.; Bernstein, N.; Wilkins, D. M.; Ceriotti, M.; Cs \'a nyi, G. Gaussian Process Regression for Materials and Molecules . Chem. Rev. 2021, 121, 10073--10141

    2021

  9. [17]

    P.; Ortner, C.; Cs \'a nyi, G.; Ceriotti, M

    Musil, F.; Grisafi, A.; Bart \'o k, A. P.; Ortner, C.; Cs \'a nyi, G.; Ceriotti, M. Physics-Inspired Structural Representations for Molecules and Materials . Chem. Rev. 2021, 121, 9759--9815

    2021

  10. [18]

    W.; Behler, J

    Kocer, E.; Ko, T. W.; Behler, J. Neural Network Potentials: A Concise Overview of Methods . Annu. Rev. Phys. Chem. 2022, 73, 163--186

    2022

  11. [19]

    G.; Margraf, J

    Timmermann, J.; Lee, Y.; Staacke, C. G.; Margraf, J. T.; Scheurer, C.; Reuter, K. Data-Efficient Iterative Training of Gaussian Approximation Potentials: Application to Surface Structure Determination of Rutile IrO _ 2 and RuO _ 2 . J. Chem. Phys. 2021, 155, 244107

    2021

  12. [20]

    Lee, Y.; Timmermann, J.; Panosetti, C.; Scheurer, C.; Reuter, K. Staged Training of Machine-Learning Potentials from Small to Large Surface Unit Cells: Efficient Global Structure Determination of the RuO _ 2 (100)-c(2 2) Reconstruction and (410) Vicinal . J. Phys. Chem. C 2023...

    2023

  13. [21]

    Zeng, Z.; Chan, M. K. Y.; Zhao, Z.-J.; Kubal, J.; Fan, D.; Greeley, J. Towards First Principles-Based Prediction of Highly Accurate Electrochemical Pourbaix Diagrams . J. Phys. Chem. C 2015, 119, 18177--18187

    2015

  14. [22]

    R.; Rondinelli, J

    Huang, L.-F.; Scully, J. R.; Rondinelli, J. M. Modeling Corrosion with First-Principles Electrochemical Phase Diagrams . Annu. Rev. Mater. Res. 2019, 49, 53--77

    2019

  15. [23]

    Hamann, D. R. Optimized Norm-Conserving Vanderbilt Pseudopotentials . Phys. Rev. B 2013, 88, 085117

    2013

  16. [24]

    Giannozzi, P. et al. Quantum ESPRESSO : A Modular and Open-Source Software Project for Quantum Simulations of Materials. J. Phys. Condens. Matter 2009, 21, 395502

    2009

  17. [25]

    P.; Burke, K.; Ernzerhof, M

    Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized Gradient Approximation Made Simple . Phys. Rev. Lett. 1996, 77, 3865--3868

    1996

  18. [26]

    Role of Point Defects in Enhancing the Conductivity of BiVO _ 4

    Seo, H.; Ping, Y.; Galli, G. Role of Point Defects in Enhancing the Conductivity of BiVO _ 4 . Chem. Mater. 2018, 30, 7793--7802

    2018

  19. [27]

    J.; Lee, D.; Zhou, C.; Kawasaki, J

    Wang, W.; Strohbeen, P. J.; Lee, D.; Zhou, C.; Kawasaki, J. K.; Choi, K.-S.; Liu, M.; Galli, G. The Role of Surface Oxygen Vacancies in BiVO _4 . Chem. Mater. 2020, 32, 2899--2909

    2020

  20. [28]

    M.; Zhang, S.; Zhou, C.; Melani, G.; Wi, D

    Hilbrands, A. M.; Zhang, S.; Zhou, C.; Melani, G.; Wi, D. H.; Lee, D.; Xi, Z.; Head, A. R.; Liu, M.; Galli, G.; Choi, K.-S. Impact of Varying the Photoanode/Catalyst Interfacial Composition on Solar Water Oxidation: The Case of BiVO _ 4 (010)/FeOOH Photoanodes . J. Am. Chem. S...

    2023

  21. [29]

    Broyden, C. G. The Convergence of a Class of Double-Rank Minimization Algorithms: 2. The New Algorithm . IMA J. Appl. Math. 1970, 6, 222--231

    1970

  22. [30]

    A Family of Variable-Metric Methods Derived by Variational Means

    Goldfarb, D. A Family of Variable-Metric Methods Derived by Variational Means . Math. Comput. 1970, 24, 23--26

    1970

  23. [31]

    Shanno, D. F. Conditioning of Quasi-Newton Methods for Function Minimization . Math. Comput. 1970, 24, 647--656

    1970

  24. [32]

    Comprehensive modeling of the band gap and absorption spectrum of BiVO _ 4

    Wiktor, J.; Reshetnyak, I.; Ambrosio, F.; Pasquarello, A. Comprehensive modeling of the band gap and absorption spectrum of BiVO _ 4 . Phys. Rev. Mater. 2017, 1, 022401

    2017

  25. [33]

    Strong Hole Trapping due to Oxygen Dimers in BiVO _ 4 : Effect on the Water Oxidation Reaction

    Ambrosio, F.; Wiktor, J. Strong Hole Trapping due to Oxygen Dimers in BiVO _ 4 : Effect on the Water Oxidation Reaction . J. Phys. Chem. Lett. 2019, 10, 7113--7118

    2019

  26. [34]

    Robust Periodic Hartree-Fock Exchange for Large-Scale Simulations Using Gaussian Basis Sets

    Guidon, M.; Hutter, J.; VandeVondele , J. Robust Periodic Hartree-Fock Exchange for Large-Scale Simulations Using Gaussian Basis Sets . J. Chem. Theory Comput. 2009, 5, 3010--3021

    2009

  27. [35]

    Auxiliary Density Matrix Methods for Hartree-Fock Exchange Calculations

    Guidon, M.; Hutter, J.; VandeVondele , J. Auxiliary Density Matrix Methods for Hartree-Fock Exchange Calculations . J. Chem. Theory Comput. 2010, 6, 2348--2364

    2010

  28. [36]

    u hne, T. D.; Iannuzzi, M.; Del Ben , M.; Rybkin, V. V.; Seewald, P.; Stein, F.; Laino, T.; Khaliullin, R. Z.; Sch \

    K \"u hne, T. D.; Iannuzzi, M.; Del Ben , M.; Rybkin, V. V.; Seewald, P.; Stein, F.; Laino, T.; Khaliullin, R. Z.; Sch \"u tt, O.; Schiffmann, F.; others CP2K: An Electronic Structure and Molecular Dynamics Software Package--Quickstep: Efficient and Accurate Electronic Structu...

    2020

  29. [37]

    Separable Dual-Space Gaussian Pseudopotentials

    Goedecker, S.; Teter, M.; Hutter, J. Separable Dual-Space Gaussian Pseudopotentials . Phys. Rev. B 1996, 54, 1703

    1996

  30. [38]

    Gaussian Basis Sets for Accurate Calculations on Molecular Systems in Gas and Condensed Phases

    VandeVondele, J.; Hutter, J. Gaussian Basis Sets for Accurate Calculations on Molecular Systems in Gas and Condensed Phases . J. Chem. Phys. 2007, 127, 114105

    2007

  31. [39]

    Electron and Hole Polarons at the BiVO _ 4 --Water Interface

    Wiktor, J.; Pasquarello, A. Electron and Hole Polarons at the BiVO _ 4 --Water Interface . 2019; https://doi.org/10.24435/materialscloud:2019.0035/v1

    2019

  32. [40]

    A Consistent and Accurate Ab Initio Parametrization of Density Functional Dispersion Correction (DFT-D) for the 94 Elements H-Pu

    Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. A Consistent and Accurate Ab Initio Parametrization of Density Functional Dispersion Correction (DFT-D) for the 94 Elements H-Pu . J. Chem. Phys. 2010, 132

    2010

  33. [41]

    H.; Mortensen, J

    Larsen, A. H.; Mortensen, J. J.; Blomqvist, J.; Castelli, I. E.; Christensen, R.; Du ak, M.; Friis, J.; Groves, M. N.; Hammer, B.; Hargus, C.; others The Atomic Simulation Environment--A Python Library for Working with Atoms . J. Phys.: Condens. Matter. 2017, 29, 273002

    2017

  34. [42]

    P.; Payne, M

    Bart \'o k, A. P.; Payne, M. C.; Kondor, R.; Cs \'a nyi, G. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons . Phys. Rev. Lett. 2010, 104, 136403

    2010

  35. [43]

    P.; Kondor, R.; Cs \'a nyi, G

    Bart \'o k, A. P.; Kondor, R.; Cs \'a nyi, G. On Representing Chemical Environments . Phys. Rev. B 2013, 87, 184115

    2013

  36. [44]

    Fast Parallel Algorithms for Short-Range Molecular Dynamics

    Plimpton, S. Fast Parallel Algorithms for Short-Range Molecular Dynamics . J. Comput. Phys. 1995, 117, 1--19

    1995

  37. [45]

    C.; Andersen, H

    Swope, W. C.; Andersen, H. C.; Berens, P. H.; Wilson, K. R. A Computer Simulation Method for the Calculation of Equilibrium Constants for the Formation of Physical Clusters of Molecules: Application to Small Water Clusters . J. Chem. Phys. 1982, 76, 637--649

    1982

  38. [46]

    Berendsen, H. J. C.; van Postma , J. P. M.; van Gunsteren , W. F.; DiNola, A.; Haak, J. R. Molecular Dynamics with Coupling to an External Bath . J. Chem. Phys. 1984, 81, 3684--3690

    1984

  39. [47]

    Rettie, A. J. E.; Lee, H. C.; Marshall, L. G.; Lin, J.-F.; Capan, C.; Lindemuth, J.; McCloy, J. S.; Zhou, J.; Bard, A. J.; Mullins, C. B. Combined Charge Carrier Transport and Photoelectrochemical Characterization of BiVO _ 4 Single Crystals: Intrinsic Behavior of a Complex Me...

    2013

  40. [48]

    Zhong, M.; Hisatomi, T.; Kuang, Y.; Zhao, J.; Liu, M.; Iwase, A.; Jia, Q.; Nishiyama, H.; Minegishi, T.; Nakabayashi, M.; others Surface Modification of CoO _ x Loaded BiVO _ 4 Photoanodes with Ultrathin p -Type NiO Layers for Improved Solar Water Oxidation . J. Am. Chem. Soc....

    2015

  41. [49]

    Enhanced Surface Reaction Kinetics and Charge Separation of p -- n Heterojunction Co _ 3 O _ 4 /BiVO _ 4 Photoanodes

    Chang, X.; Wang, T.; Zhang, P.; Zhang, J.; Li, A.; Gong, J. Enhanced Surface Reaction Kinetics and Charge Separation of p -- n Heterojunction Co _ 3 O _ 4 /BiVO _ 4 Photoanodes . J. Am. Chem. Soc. 2015, 137, 8356--8359

    2015

  42. [50]

    H.; Amal, R.; Kudo, A

    Iwase, A.; Yoshino, S.; Takayama, T.; Ng, Y. H.; Amal, R.; Kudo, A. Water Splitting and CO _ 2 Reduction under Visible Light Irradiation using Z-Scheme Systems Consisting of Metal Sulfides, CoO _ x -Loaded BiVO _ 4 , and a Reduced Graphene Oxide Electron Mediator . J. Am. Chem...

    2016

  43. [51]

    V.; Hammarström, L.; Selli, E

    Grigioni, I.; Abdellah, M.; Corti, A.; Dozzi, M. V.; Hammarström, L.; Selli, E. Photoinduced Charge-Transfer Dynamics in WO _ 3 /BiVO _ 4 Photoanodes Probed through Midinfrared Transient Absorption Spectroscopy . J. Am. Chem. Soc. 2018, 140, 14042--14045

    2018

  44. [52]

    R.; Francas, L.; Sachs, M.; Moss, B.; Corby, S.; Mesa, C

    Selim, S.; Pastor, E.; Garcia-Tecedor, M.; Morris, M. R.; Francas, L.; Sachs, M.; Moss, B.; Corby, S.; Mesa, C. A.; Gimenez, S.; others Impact of Oxygen Vacancy Occupancy on Charge Carrier Dynamics in BiVO _ 4 Photoanodes . J. Am. Chem. Soc. 2019, 141, 18791--18798

    2019

  45. [53]

    R.; Bakulin, A

    Meng, Z.; Pastor, E.; Selim, S.; Ning, H.; Maimaris, M.; Kafizas, A.; Durrant, J. R.; Bakulin, A. A. Operando IR Optical Control of Localized Charge Carriers in BiVO _ 4 Photoanodes . J. Am. Chem. Soc. 2023, 145, 17700--17709

    2023

  46. [54]

    I.; Tian, L.; Zhou, G.; Selim, S.; Steier, L.; Durrant, J

    Li, B.; Oldham, L. I.; Tian, L.; Zhou, G.; Selim, S.; Steier, L.; Durrant, J. R. Electrochemical versus Photoelectrochemical Water Oxidation Kinetics on Bismuth Vanadate (Photo)anodes . J. Am. Chem. Soc. 2024, 146, 12324--12328

    2024

  47. [55]

    Structural Analysis of Amorphous V _ 2 O _ 5 by Large-Angle X-Ray Scattering

    Mosset, A.; Lecante, P.; Galy, J.; Livage, J. Structural Analysis of Amorphous V _ 2 O _ 5 by Large-Angle X-Ray Scattering . Philos. Mag. B 1982, 46, 137--149

    1982

  48. [56]

    Wagner, M.; Planer, J.; Heller, B. S. J.; Langer, J.; Limbeck, A.; Boatner, L. A.; Steinr \"u ck, H.-P.; Redinger, J.; Maier, F.; Mittendorfer, F.; others Oxygen-Rich Tetrahedral Surface Phase on High-Temperature rutile VO _ 2 (110) Single Crystals . Phys. Rev. Mater. 2021, 5, 125001

    2021

  49. [57]

    / cI A Kƀ K\ 2<5 C)J ]^ I) ( i=

    Binninger, T.; Doublet, M.-L. The Ir--OOOO--Ir Transition State and the Mechanism of the Oxygen Evolution Reaction on IrO _ 2 (110) . Energy Environ. Sci. 2022, 15, 2519--2528 mcitethebibliography manuscript.tex0000664000000000000000000012676014726204030012477 0ustar rootroot ...

    2022

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.