Pith. sign in

REVIEW 3 major objections 4 minor 7 cited by

A new machine-learning dataset of 7.8 million solid-liquid interface calculations is used to claim that CO dimerization on copper is only weakly affected by surface charge and cation identity, except at very negative charges, and that stepp

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-04 15:48 UTC pith:4S6SGRCJ

load-bearing objection OC25 is a genuine dataset contribution, but the abstract's dimerization free-energy results are absent from the body—the paper is two different documents stapled together. the 3 major comments →

arxiv 2509.17862 v2 pith:4S6SGRCJ submitted 2025-09-22 cond-mat.mtrl-sci physics.chem-ph

Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations

Sushree Jagriti Sahoo , Mikael Maraschin , Joel B Varley , Daniel S. Levine , Zachary Ulissi , C. Lawrence Zitnick , Wayu Takemura , Joseph A. Gauthier
show 2 more authors
Nitish Govindarajan Muhammed Shuaibi
This is my paper
classification cond-mat.mtrl-sci physics.chem-ph
keywords solid-liquid interfacesmachine learning interatomic potentialsCO dimerizationelectrocatalysisCO2 electroreductionexplicit solvationgraph neural networksopen dataset
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper aims to close a gap in open catalyst datasets: none had captured solid-liquid interfaces with explicit solvent and ions. It introduces OC25, a dataset of 7.8 million DFT single-point calculations across 1.5 million unique solvated surface configurations, and trains graph-neural-network potentials that reach 0.1 eV energy and 0.015 eV/Å force accuracy. As a demonstration, the abstract reports free-energy profiles of CO dimerization on copper from large explicit-solvent simulations, finding the reaction weakly sensitive to surface charge and cation identity except at strongly negative charge, and more favorable on stepped Cu(310). If these findings hold, they would guide how CO2 electroreduction catalysts should be designed and show that open ML potentials make such simulations routine.

Core claim

The paper's central claim is that machine-learned potentials trained on the OC25 dataset become practical tools for explicit-solvent electrocatalysis, and that their application to CO dimerization on Cu surfaces shows weak sensitivity to surface charge and cation identity, with appreciable stabilization only at the most negative charge densities, while the stepped Cu(310) surface provides a more favorable pathway at modest reducing potentials.

What carries the argument

The central object is the OC25 dataset: 7,801,261 density-functional-theory energies and forces sampled from off-equilibrium configurations of 88 elements, eight solvents, nine ions, and 98 adsorbates at solid-liquid interfaces. The models trained on it are graph neural network potentials; their predicted energies and forces are accurate to about 0.1 eV and 0.015 eV/Å, and they are used to run explicit-solvent simulations with cells of more than 800 atoms for up to 7 ns. A drift-filtering criterion of 1 eV/Å and a pseudo-solvation-energy metric round out the methodology.

Load-bearing premise

The entire CO dimerization conclusion rests on the unstated assumption that ML potentials trained on roughly 144-atom off-equilibrium configurations, with energy errors around 0.1 eV, remain accurate enough in much larger explicit-solvent cells under electrochemical conditions to resolve the small free-energy differences between facets and charge states; the paper does not validate this transfer.

What would settle it

Reproduce the claimed 7-ns enhanced-sampling simulation on Cu(100) and Cu(310) using the released OC25 checkpoint, and confirm that the reported free-energy ordering survives; then run conventional AIMD on one representative state to check whether the 0.1 eV model error flips the barrier difference. A simpler check: the body of the paper contains none of the dimerization free-energy profiles, so finding their data or scripts would be the first confirmation.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • If the models' transferability holds, electrocatalytic transformations at solid-liquid interfaces can be simulated with explicit solvent at timescales orders of magnitude beyond ab initio methods.
  • The claimed weak charge and cation sensitivity of CO dimerization implies that cation promotion in CO2 reduction must act on other steps, not the initial C–C coupling.
  • The stepped-facet result suggests geometric surface engineering, not just electrolyte tuning, is a way to lower the dimerization barrier.
  • The force-drift analysis indicates that moderately loose DFT convergence can be used in training without degrading force accuracy, lowering the cost of future datasets.
  • Making OC25 publicly available lets the community benchmark and improve models on solid-liquid interfaces directly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A reader checking the paper will notice the body is devoted to dataset construction and benchmark tables; the CO dimerization free-energy profiles reported in the abstract do not appear in the manuscript, so reproducing them from the released models and dataset is the immediate next step.
  • If the weak cation dependence holds, the well-known alkali-cation effects on CO2 reduction selectivity would have to operate on later steps, such as protonation or desorption, rather than on the dimerization transition state.
  • The dataset's off-equilibrium sampling strategy could be reused to train potentials for other charged interfaces, such as battery electrode/electrolyte systems.
  • The claimed step-facet advantage, if robust, connects to experimental observations that roughened or defect-rich copper surfaces often shift product selectivity, making facet-resolved kinetic models a testable extension.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The manuscript introduces the Open Catalyst 2025 (OC25) dataset for solid-liquid interfaces, containing over 7.8 million single-point DFT calculations across 1.5 million explicit-solvent environments, and reports baseline machine-learned interatomic potential results. The body of the paper covers dataset construction, DFT settings and force-convergence filtering, train/validation/test splits including out-of-distribution solvent/ion splits, and baseline energy/force errors for eSEN and UMA models. The title and abstract, however, claim a concrete application: CO dimerization on Cu surfaces, with large (>800-atom) explicit-solvent cells, enhanced sampling up to 7 ns, free-energy profiles as functions of surface charge, cation identity, and facet, and specific findings about weak charge/cation sensitivity and more favorable pathways on Cu(310). This application and its results are entirely absent from the manuscript body, which instead concludes in Section 4 that interfacial reactivity predictions 'remain to be tested in future studies.'

Significance. If the OC25 dataset and baseline models are made openly available as described, the dataset could be a valuable community resource for training and benchmarking machine learning potentials at solid-liquid interfaces. The force-convergence analysis (Section 2.2.4, Figure 4) is a useful contribution, and the OOD splits are thoughtfully designed. However, the advertised central scientific result — the CO dimerization study — is not present, so the paper cannot currently support its title or abstract. The significance of the work as submitted is therefore limited to the dataset report, not to the claimed electrocatalytic insights.

major comments (3)
  1. [Abstract, Sections 2–4] The abstract's central claim — 'We find that dimerization is weakly sensitive to charge and cation identity, with appreciable stabilization only at the most negative charge densities, while extension to stepped Cu(310) reveals a more favorable pathway at modest reducing potentials' — is absent from the manuscript body. Sections 2 and 3 describe dataset construction and baseline model errors only; Section 4 explicitly states that 'these aspects remain to be tested in future studies.' No section, figure, table, or equation reports CO dimerization free-energy profiles, >800-atom simulations, enhanced sampling, surface charge variation, cation identity effects, or Cu(310) facet results. This is not a minor omission; it is the advertised central result.
  2. [Section 2.1 and 2.2.3 vs. Abstract] The transferability premise for the claimed application is unvalidated. OC25 structures are described as roughly 144-atom systems sampled from short high-temperature (1000 K) AIMD or 5-step relaxations. The abstract claims the models enable 'large cells (>800 atoms)' and 'enhanced sampling up to 7 ns' for CO dimerization, but no evidence is presented that models trained on these small off-equilibrium configurations remain accurate over long MD trajectories, at electrochemical conditions, or for system sizes five or more times larger. Baseline OOD errors in Table 2 are substantially larger than in-distribution errors, and no long-timescale stability or free-energy convergence test is reported.
  3. [Section 4] The Outlook itself contradicts the abstract. After stating that models 'may still be able to accurately predict interfacial properties and reactivity,' the text says 'these aspects remain to be tested in future studies.' This directly undermines the abstract's presentation of the CO dimerization results as completed findings. The paper should either include the missing study or be reframed and retitled as a dataset paper.
minor comments (4)
  1. [Abstract] Typo: 'significantly lower than than the recently released' has a duplicated 'than'.
  2. [Section 2.3.1 and Appendix B.3] Equation (2) in Appendix B.3 repeats Equation (1) verbatim with different notation; the pseudo-solvation energy is defined twice. Consolidate to avoid confusion.
  3. [Sections 3.2 and 4] The phrase 'surprisingly' / 'to our surprise' is used twice for the same observation about model robustness to label noise; one occurrence should be removed.
  4. [Figure 4] The text says structures with drift 'greater than 10 eV/Å' have much larger errors, then a threshold of '1 eV/Å' is selected. This is understandable but the relation between the two values should be stated explicitly to avoid apparent inconsistency.

Circularity Check

0 steps flagged

No circularity in the OC25 dataset evaluation; the advertised CO-dimerization results are absent from the body, which is an unsupported-claim issue rather than a circular derivation.

full rationale

The core dataset and model evaluation are self-contained and non-circular: models are trained on the OC25 training split and evaluated against held-out OOD validation/test splits computed with tighter DFT convergence (EDIFF=10^-6 eV), and the reported energy/force/solvation MAEs are measured benchmark errors, not fitted parameters renamed as predictions. The pseudo-solvation energy definitions (Eqs. 1 and 2) define benchmark labels from DFT snapshots, not derived physical conclusions. Self-citations to OC20, OC22, UMA, and eSEN are background/model baselines and are not used as load-bearing justification for the paper's own findings. The abstract's central claim—'Using large cells (>800 atoms) and enhanced sampling up to 7 ns ... we compute free-energy profiles under varied surface charge, cation identity, and surface facet. We find that dimerization is weakly sensitive to charge and cation identity'—has no corresponding section, figure, or table in the manuscript body. Section 4 explicitly states the opposite: 'Although the models in this work may still be able to accurately predict interfacial properties and reactivity, these aspects remain to be tested in future studies.' This is a serious abstract/body mismatch and an unsupported claim, but it is not a circular step: the missing CO-dimerization result is not an input to the dataset evaluation, and no equation or self-citation reduces the claimed finding to its own inputs. Accordingly, the circularity score is 0; the mismatch should be treated as a correctness/verifiability problem rather than as circular reasoning.

Axiom & Free-Parameter Ledger

2 free parameters · 4 axioms · 0 invented entities

The central claim, if it were present, would rest on the transferability of MLIPs to a specific reactive system not in the training set. The dataset itself rests on DFT ground-truth assumptions.

free parameters (2)
  • Force drift filter threshold = 1 eV/Å
    Chosen by hand in Section 2.2.4 to decide which DFT calculations enter the OC25 training set; affects all labels.
  • Electronic convergence threshold for training data (EDIFF) = 1e-4 eV
    Set to balance cost and accuracy; the paper shows it introduces force noise that models partly overcome. This is a load-bearing data-quality choice.
axioms (4)
  • domain assumption RPBE-D3 DFT is an adequate ground truth for interfacial energies and forces at solid-liquid interfaces.
    Used for all OC25 labels (Section 2.2.3); no benchmark against higher-level theory or experimental solvation energies is provided.
  • domain assumption Spin-unpolarized calculations are adequate for the sampled surfaces and adsorbates.
    Stated in Section 2.2.3 and Section 4 as a limitation for magnetic materials.
  • domain assumption MLIPs trained on OC25 generalize to unseen bulk-solvent combinations and to larger, longer, near-equilibrium simulations.
    This is the core premise of the claimed CO dimerization application; the body does not validate it for the dimerization system.
  • domain assumption Force drift filtering by total drift < 1 eV/Å yields cleaner training labels.
    Argued from Figure 4; the threshold is heuristic.

pith-pipeline@v1.3.0-alltime-deepseek · 15319 in / 12743 out tokens · 97236 ms · 2026-08-04T15:48:29.811586+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations." pith.science (2026). https://pith.science/paper/4S6SGRCJ

@misc{pith2026250917862,
  author       = {Pith},
  title        = {Pith review of: Insights into CO dimerization at electrified Cu interfaces from large-scale machine learning simulations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4S6SGRCJ}},
  note         = {Machine review of arXiv:2509.17862}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Catalysis at solid-liquid interfaces underpins many energy technologies, yet ab initio simulations that capture interfacial dynamics remain prohibitively expensive. Here we introduce Open Catalyst 2025 (OC25), the largest dataset for solid-liquid interfaces. To demonstrate OC25-trained models as practical tools for electrocatalysis, we investigate CO dimerization on Cu surfaces, a key step in CO$_2$ electroreduction. Using large cells (>800 atoms) and enhanced sampling up to 7 ns - the largest explicit-solvent CO dimerization study to date - we compute free-energy profiles under varied surface charge, cation identity, and surface facet. We find that dimerization is weakly sensitive to charge and cation identity, with appreciable stabilization only at the most negative charge densities, while extension to stepped Cu(310) reveals a more favorable pathway at modest reducing potentials. Our results demonstrate that OC25-trained models provide a scalable tool for investigating electrocatalytic transformations at solid-liquid interfaces, enabling simulations orders of magnitude beyond ab initio methods.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin

    cond-mat.mtrl-sci 2026-06 unverdicted novelty 7.0

    CatDT deploys a self-evolving multi-agent system with UniMech and reinforcement learning to build digital twins of heterogeneous catalysts, matching experimental rates within 0.5-2x on seven benchmarks and identifying...

  2. Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin

    cond-mat.mtrl-sci 2026-06 conditional novelty 7.0

    A self-evolving multi-agent catalysis digital twin (CatDT) matches seven gas–solid kinetic benchmarks within ~0.5–2× experiment and computationally proposes non-precious PDH candidates including Ni@ZrO2.

  3. Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs

    cs.LG 2026-05 unverdicted novelty 7.0

    Force-aware NTKs and chunked acquisition enable scalable, robust active learning for MLIPs, achieving lowest energy and force errors on OC20 and remaining competitive on other benchmarks.

  4. Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials

    cs.DC 2026-04 conditional novelty 7.0

    MatRIS-MoE and Janus enable efficient exascale training of billion-parameter universal interatomic potentials by addressing second-order derivative computation and communication overheads.

  5. Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs

    cs.LG 2026-05 unverdicted novelty 6.0

    Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.

  6. Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis

    physics.chem-ph 2026-05 conditional novelty 5.0

    Fine-tuned MACE MLIPs achieve lower mean absolute errors on catalytic reaction energies and barriers than from-scratch models, with a large fine-tuned model performing best on both metallic and oxide systems including...

  7. Atomistic Modeling for Electro-chemical Reactions

    physics.chem-ph 2026-07 conditional novelty 3.0

    EU-CONCERT prioritizes systematic cross-method and theory-experiment benchmarks for constant-potential DFT, solvation, free-energy sampling, and machine-learning potentials at electrified solid-liquid interfaces.

Reference graph

Works this paper leans on

88 extracted references · 7 linked inside Pith · cited by 5 Pith papers

  1. [1]

    Investigating the error imbalance of large-scale machine learning potentials in catalysis.Catalysis Science & Technology, 14(20): 5899–5908, 2024

    Kareem Abdelmaqsoud, Muhammed Shuaibi, Adeesh Kolluru, Raffaele Cheula, and John R Kitchin. Investigating the error imbalance of large-scale machine learning potentials in catalysis.Catalysis Science & Technology, 14(20): 5899–5908, 2024

    2024

  2. [2]

    Open catalyst experiments 2024 (ocx24): Bridging experiments and computational models.arXiv preprint arXiv:2411.11783, 2024

    Jehad Abed, Jiheon Kim, Muhammed Shuaibi, Brook Wander, Boris Duijf, Suhas Mahesh, Hyeonseok Lee, Vahe Gharakhanyan, Sjoerd Hoogland, Erdem Irtem, et al. Open catalyst experiments 2024 (ocx24): Bridging experiments and computational models.arXiv preprint arXiv:2411.11783, 2024

    Pith/arXiv arXiv 2024

  3. [3]

    Low- temperature sodium-ion batteries: Challenges and progress.Advanced Energy Materials, 14(17):2303788, 2024

    Zhongchao Bai, Qian Yao, Mingyue Wang, Weijia Meng, Shixue Dou, Hua kun Liu, and Nana Wang. Low- temperature sodium-ion batteries: Challenges and progress.Advanced Energy Materials, 14(17):2303788, 2024

    2024

  4. [4]

    Towards an accelerated decarbonization of the chemical industry by electrolysis.Energy Advances, 2(2):268–279, 2023

    Magda H Barecka and Joel W Ager. Towards an accelerated decarbonization of the chemical industry by electrolysis.Energy Advances, 2(2):268–279, 2023

    2023

  5. [5]

    Open materials 2024 (omat24) inorganic materials dataset and models

    Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C Lawrence Zitnick, and Zachary W Ulissi. Open materials 2024 (omat24) inorganic materials dataset and models. arXiv preprint arXiv:2410.12771, 2024

    Pith/arXiv arXiv 2024

  6. [6]

    Ilyes Batatia, Dávid Péter Kovács, Gregor N. C. Simm, Christoph Ortner, and Gábor Csányi. Mace: Higher order equivariant message passing neural networks for fast and accurate force fields.arXiv preprint arXiv:2206.07697v2, 2023

    Pith/arXiv arXiv 2023

  7. [7]

    E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.Nature Communications, 13(1):2453, 2022

    Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky. E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.Nature Communications, 13(1):2453, 2022

    2022

  8. [8]

    Dipole correction for surface supercell calculations.Physical Review B, 59(19):12301, 1999

    Lennart Bengtsson. Dipole correction for surface supercell calculations.Physical Review B, 59(19):12301, 1999

    1999

  9. [9]

    Ngene 2022: Electrochemistry for decarbonization

    Jordi Cabana, Thomas Alaan, George W Crabtree, Po-Wei Huang, Akash Jain, Megan Murphy, Jeanne N’Diaye, Kasinath Ojha, George Agbeworvi, Helen Bergstrom, et al. Ngene 2022: Electrochemistry for decarbonization. ACS Energy Letters, 8(1):740–747, 2022

    2022

  10. [10]

    Challenges and industrial perspectives on the development of sodium ion batteries.Nano Energy, 129:110052, 2024

    Xiaosheng Cai, Yingying Yue, Zheng Yi, Junfei Liu, Yangping Sheng, and Yuhao Lu. Challenges and industrial perspectives on the development of sodium ion batteries.Nano Energy, 129:110052, 2024

    2024

  11. [11]

    Open catalyst 2020 (oc20) dataset and community challenges.ACS Catalysis, 11(10):6059–6072, 2021

    Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al. Open catalyst 2020 (oc20) dataset and community challenges.ACS Catalysis, 11(10):6059–6072, 2021

    2020

  12. [12]

    Xiaoting Chen, Ian T McCrum, Kathleen A Schwarz, Michael J Janik, and Marc TM Koper. Co-adsorption of cations as the cause of the apparent ph dependence of hydrogen adsorption on a stepped platinum single-crystal electrode.Angewandte Chemie International Edition, 56(47):15025–15029, 2017

    2017

  13. [13]

    Direct propylene epoxidation via water activation over pd-pt electrocatalysts.Science, 383(6678):49–55, 2024

    Minju Chung, Joseph H Maalouf, Jason S Adams, Chenyu Jiang, Yuriy Román-Leshkov, and Karthish Manthiram. Direct propylene epoxidation via water activation over pd-pt electrocatalysts.Science, 383(6678):49–55, 2024

    2024

  14. [14]

    Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling.Nature Machine Intelligence, 5(9):1031–1041, 2023

    Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han, Christopher J Bartel, and Gerbrand Ceder. Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling.Nature Machine Intelligence, 5(9):1031–1041, 2023

    2023

  15. [15]

    Side reactions/changes in lithium-ion batteries: mechanisms and strategies for creating safer and better batteries.Advanced Materials, 36(29):2401482, 2024

    Hao Du, Yadong Wang, Yuqiong Kang, Yun Zhao, Yao Tian, Xianshu Wang, Yihong Tan, Zheng Liang, John Wozny, Tao Li, et al. Side reactions/changes in lithium-ion batteries: mechanisms and strategies for creating safer and better batteries.Advanced Materials, 36(29):2401482, 2024

    2024

  16. [16]

    Spice, a dataset of drug-like molecules and peptides for training machine learning potentials.Scientific Data, 10(1):11, 2023

    Peter Eastman, Pavan Kumar Behara, David L Dotson, Raimondas Galvelis, John E Herr, Josh T Horton, Yuezhi Mao, John D Chodera, Benjamin P Pritchard, Yuanqing Wang, et al. Spice, a dataset of drug-like molecules and peptides for training machine learning potentials.Scientific Data, 10(1):11, 2023

    2023

  17. [17]

    Calcium-mediated nitrogen reduction for electrochemical ammonia synthesis.Nature Materials, 23(1):101–107, 2024

    Xianbiao Fu, Valerie A Niemann, Yuanyuan Zhou, Shaofeng Li, Ke Zhang, Jakob B Pedersen, Mattia Saccoccio, Suzanne Z Andersen, Kasper Enemark-Rasmussen, Peter Benedek, et al. Calcium-mediated nitrogen reduction for electrochemical ammonia synthesis.Nature Materials, 23(1):101–107, 2024

    2024

  18. [18]

    Learning smooth and expressive interatomic potentials for physical property prediction.arXiv preprint arXiv:2502.12147, 2025

    Xiang Fu, Brandon M Wood, Luis Barroso-Luque, Daniel S Levine, Meng Gao, Misko Dzamba, and C Lawrence Zitnick. Learning smooth and expressive interatomic potentials for physical property prediction.arXiv preprint arXiv:2502.12147, 2025. 11

    Pith/arXiv arXiv 2025

  19. [19]

    Gemnet-oc: developing graph neural networks for large and diverse molecular simulation datasets.arXiv preprint arXiv:2204.02782, 2022

    Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C Lawrence Zitnick, and Abhishek Das. Gemnet-oc: developing graph neural networks for large and diverse molecular simulation datasets.arXiv preprint arXiv:2204.02782, 2022

    Pith/arXiv arXiv 2022

  20. [20]

    Green hydrogen production: From lab scale to pilot scale photocatalysis

    Ashrumochan Gouda, Devendra Sharma, Ashish Kumar, and Venkata Krishnan. Green hydrogen production: From lab scale to pilot scale photocatalysis. InTowards Sustainable and Green Hydrogen Production by Photocatalysis: Scalability Opportunities and Challenges (Volume 1), pages 185–210. ACS Publications, 2024

    2024

  21. [21]

    Gauthier, Jun Cheng, Ivo Filot, Arthur Hagopian, Heine Anton Hansen, Jun Huang, Piotr M

    Nitish Govindarajan, Georg Kastlunger, Joseph A. Gauthier, Jun Cheng, Ivo Filot, Arthur Hagopian, Heine Anton Hansen, Jun Huang, Piotr M. Kowalski, Jinwen Liu, Juan M. Lombardi, Mikael Maraschin, Andrew Peterson, Hemanth S. Pillai, Hector Prats, Conor J. Price, René van Roij, Jan Rossmeisl, Ranga Rohit Seemakurthi, Seung-Jae Shin, Audrey Smith, Jia-Xin Zh...

    2025

  22. [22]

    Metal nitride as a mediator for the electrochemical synthesis of nh3.ACS Energy Letters, 9(8):4188–4195, 2024

    Ishita Goyal, Nishithan C Kani, Samuel A Olusegun, Sreenivasulu Chinnabattigalla, Rajan R Bhawnani, Ksenija D Glusac, Aayush R Singh, Joseph A Gauthier, and Meenesh R Singh. Metal nitride as a mediator for the electrochemical synthesis of nh3.ACS Energy Letters, 9(8):4188–4195, 2024

    2024

  23. [23]

    Stefan Grimme, Jens Antony, Stephan Ehrlich, and Helge Krieg. A consistent and accurate ab initio parametrization of density functional dispersion correction (dft-d) for the 94 elements h-pu.The Journal of Chemical Physics, 132 (15):154104, 2010

    2010

  24. [24]

    Ab initio simulations of water/metal interfaces.Chemical Reviews, 122(12): 10746–10776, 2022

    Axel Groß and Sung Sakong. Ab initio simulations of water/metal interfaces.Chemical Reviews, 122(12): 10746–10776, 2022

    2022

  25. [25]

    J. Guo, M. Calegari Andrade, C. Hahn, A. Kulkarni, and N. Govindarajan. Understanding cation and surface charging effects at electrified interfaces using neural network interatomic potentials.ChemRxiv, 2025

    2025

  26. [26]

    Improved adsorption energetics within density- functional theory using revised perdew-burke-ernzerhof functionals.Physical Review B, 59(11):7413, 1999

    Bjørk Hammer, Lars Bruno Hansen, and Jens Kehlet Nørskov. Improved adsorption energetics within density- functional theory using revised perdew-burke-ernzerhof functionals.Physical Review B, 59(11):7413, 1999

    1999

  27. [27]

    Recent advancements in electrochemical sensors based on mofs and their derivatives.Small, 21(4):2408624, 2025

    Xi Hao, Weihua Song, Yinghui Wang, Jieling Qin, and Zhenqi Jiang. Recent advancements in electrochemical sensors based on mofs and their derivatives.Small, 21(4):2408624, 2025

    2025

  28. [28]

    Harraz, Kunal M

    Deiaa M. Harraz, Kunal M. Lodaya, Bryan Y. Tang, and Yogesh Surendranath. Homogeneous-heterogeneous bifunctionality in pd-catalyzed vinyl acetate synthesis.Science, 388(6742):eads7913, 2025

    2025

  29. [29]

    Solvation at metal/water interfaces: An ab initio molecular dynamics benchmark of common computational approaches.The Journal of Chemical Physics, 152(14), 2020

    Hendrik H Heenen, Joseph A Gauthier, Henrik H Kristoffersen, Thomas Ludwig, and Karen Chan. Solvation at metal/water interfaces: An ab initio molecular dynamics benchmark of common computational approaches.The Journal of Chemical Physics, 152(14), 2020

    2020

  30. [30]

    Machine learning and data-driven methods in computational surface and interface science.npj Computational Materials, 11(1):196, 2025

    Lukas Hörmann, Wojciech G Stark, and Reinhard J Maurer. Machine learning and data-driven methods in computational surface and interface science.npj Computational Materials, 11(1):196, 2025

    2025

  31. [31]

    Methods for nitrogen activation by reduction and oxidation.Nature Reviews Methods Primers, 1(1):56, 2021

    Haldrian Iriawan, Suzanne Z Andersen, Xilun Zhang, Benjamin M Comer, Jesús Barrio, Ping Chen, Andrew J Medford, Ifan EL Stephens, Ib Chorkendorff, and Yang Shao-Horn. Methods for nitrogen activation by reduction and oxidation.Nature Reviews Methods Primers, 1(1):56, 2021

    2021

  32. [32]

    The materials project: Accelerating materials design through theory-driven data and tools

    Anubhav Jain, Joseph Montoya, Shyam Dwaraknath, Nils ER Zimmermann, John Dagdelen, Matthew Horton, Patrick Huck, Donny Winston, Shreyas Cholia, Shyue Ping Ong, et al. The materials project: Accelerating materials design through theory-driven data and tools. InHandbook of Materials Modeling: Methods: Theory and Modeling, pages 1751–1784. Springer, 2020

    2020

  33. [33]

    Aidan Klemm, Stephen P Vicchio, Sanchari Bhattacharjee, Eda Cagli, Yensil Park, Muhammad Zeeshan, Ruth Dikki, Harrison Liu, Michelle K Kidder, Rachel B Getman, et al. Impact of hydrogen bonds on co2 binding in eutectic solvents: an experimental and computational study toward sorbent design for co2 capture.ACS Sustainable Chemistry & Engineering, 11(9):374...

    2023

  34. [34]

    Efficiencyofab-initiototalenergycalculationsformetalsandsemiconductors using a plane-wave basis set.Computational Materials Science, 6(1):15–50, 1996

    GeorgKresseandJürgenFurthmüller. Efficiencyofab-initiototalenergycalculationsformetalsandsemiconductors using a plane-wave basis set.Computational Materials Science, 6(1):15–50, 1996

    1996

  35. [35]

    Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.Physical Review B, 54(16):11169–11186, 1996

    Georg Kresse and Jürgen Furthmüller. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set.Physical Review B, 54(16):11169–11186, 1996

    1996

  36. [36]

    Ab initio molecular-dynamics simulation of the liquid-metal–amorphous- semiconductor transition in germanium.Physical Review B, 49(20):14251–14269, 1994

    Georg Kresse and Jürgen Hafner. Ab initio molecular-dynamics simulation of the liquid-metal–amorphous- semiconductor transition in germanium.Physical Review B, 49(20):14251–14269, 1994. 12

    1994

  37. [37]

    From ultrasoft pseudopotentials to the projector augmented-wave method

    Georg Kresse and Daniel Joubert. From ultrasoft pseudopotentials to the projector augmented-wave method. Physical Review B, 59(3):1758, 1999

    1999

  38. [38]

    Ambarish Kulkarni, Samira Siahrostami, Anjli Patel, and Jens K. Nørskov. Understanding catalytic activity trends in the oxygen reduction reaction.Chemical Reviews, 118(5):2302–2312, 2018

    2018

  39. [39]

    Liquid phase modeling in porous media: Adsorption of methanol and ethanol in h-mfi in condensed water.Journal of Chemical Theory and Computation, 21(12):6121–6134, 2025

    Subrata Kumar Kundu, Muhammad Zeeshan, Panuwat Watthaisong, and Andreas Heyden. Liquid phase modeling in porous media: Adsorption of methanol and ethanol in h-mfi in condensed water.Journal of Chemical Theory and Computation, 21(12):6121–6134, 2025

    2025

  40. [40]

    Adsorbml: A leap in efficiency for adsorption energy calculations using generalizable machine learning potentials.npj Computational Materials, 9(1):172, 2023

    Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C Lawrence Zitnick, and Zachary W Ulissi. Adsorbml: A leap in efficiency for adsorption energy calculations using generalizable machine learning potentials.npj Computational Materials, 9(1):172, 2023

    2023

  41. [41]

    Understanding continuous lithium-mediated electrochemical nitrogen reduction.Joule, 3(4):1127–1139, 2019

    Nikifar Lazouski, Zachary J Schiffer, Kindle Williams, and Karthish Manthiram. Understanding continuous lithium-mediated electrochemical nitrogen reduction.Joule, 3(4):1127–1139, 2019

    2019

  42. [42]

    Emerging atomistic modeling methods for heterogeneous electrocatalysis.Chemical Reviews, 124(14):8620–8656, 2024

    Zachary Levell, Jiabo Le, Saerom Yu, Ruoyu Wang, Sudheesh Ethirajan, Rachita Rana, Ambarish Kulkarni, Joaquin Resasco, Deyu Lu, Jun Cheng, and Yuanyue Liu. Emerging atomistic modeling methods for heterogeneous electrocatalysis.Chemical Reviews, 124(14):8620–8656, 2024

    2024

  43. [43]

    The open molecules 2025 (omol25) dataset, evaluations, and models.arXiv preprint arXiv:2505.08762, 2025

    Daniel S Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G Taylor, Muhammad R Hasyim, Kyle Michel, Ilyes Batatia, Gábor Csányi, Misko Dzamba, Peter Eastman, et al. The open molecules 2025 (omol25) dataset, evaluations, and models.arXiv preprint arXiv:2505.08762, 2025

    arXiv 2025

  44. [44]

    Lewis, Ryan P

    Noah B. Lewis, Ryan P. Bisbey, Karl S. Westendorff, Alexander V. Soudackov, and Yogesh Surendranath. A molecular-level mechanistic framework for interfacial proton-coupled electron transfer kinetics.Nature Chemistry, 16(3):343–352, 2024

    2024

  45. [45]

    Recent advances in molecularly imprinted polymer-based electrochemical sensors.Biosensors and Bioelectronics, 249:116018, 2024

    Yixuan Li, Liuxiong Luo, Yingqi Kong, Yujia Li, Quansheng Wang, Mingqing Wang, Ying Li, Andrew Davenport, and Bing Li. Recent advances in molecularly imprinted polymer-based electrochemical sensors.Biosensors and Bioelectronics, 249:116018, 2024

    2024

  46. [46]

    Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations.arXiv preprint arXiv:2306.12059, 2023

    Yi-Lun Liao, Brandon Wood, Abhishek Das, and Tess Smidt. Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations.arXiv preprint arXiv:2306.12059, 2023

    Pith/arXiv arXiv 2023

  47. [47]

    Revealing the degradation patterns of lithium-ion batteries from impedance spectroscopy using variational auto-encoders.Energy Storage Materials, 69:103394, 2024

    Yanshuo Liu, Qiang Li, and Kai Wang. Revealing the degradation patterns of lithium-ion batteries from impedance spectroscopy using variational auto-encoders.Energy Storage Materials, 69:103394, 2024

    2024

  48. [48]

    Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, 2017

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, 2017

    Pith/arXiv arXiv 2017

  49. [49]

    Decarbonization of the chemical industry through electrification: Barriers and opportunities.Joule, 7(1):23–41, 2023

    Dharik S Mallapragada, Yury Dvorkin, Miguel A Modestino, Daniel V Esposito, Wilson A Smith, Bri-Mathias Hodge, Michael P Harold, Vincent M Donnelly, Alice Nuz, Casey Bloomquist, et al. Decarbonization of the chemical industry through electrification: Barriers and opportunities.Joule, 7(1):23–41, 2023

    2023

  50. [50]

    Packmol: A package for building initial configurations for molecular dynamics simulations.Journal of Computational Chemistry, 30(13): 2157–2164, 2009

    Leandro Martínez, Ricardo Andrade, Ernesto G Birgin, and José Mario Martínez. Packmol: A package for building initial configurations for molecular dynamics simulations.Journal of Computational Chemistry, 30(13): 2157–2164, 2009

    2009

  51. [51]

    Transferable water potentials using equivariant neural networks.The Journal of Physical Chemistry Letters, 15(14):3740–3747, 2024

    Tristan Maxson and Tibor Szilvási. Transferable water potentials using equivariant neural networks.The Journal of Physical Chemistry Letters, 15(14):3740–3747, 2024

    2024

  52. [52]

    Enhancing the quality and reliability of machine learning interatomic potentials through better reporting practices.The Journal of Physical Chemistry C, 128(16):6524–6537, 2024

    Tristan Maxson, Ademola Soyemi, Benjamin WJ Chen, and Tibor Szilvási. Enhancing the quality and reliability of machine learning interatomic potentials through better reporting practices.The Journal of Physical Chemistry C, 128(16):6524–6537, 2024

    2024

  53. [53]

    Medford, Aleksandra Vojvodic, Jens S

    Andrew J. Medford, Aleksandra Vojvodic, Jens S. Hummelshøj, Johannes Voss, Frank Abild-Pedersen, Felix Studt, Thomas Bligaard, Anders Nilsson, and Jens K. Nørskov. From the sabatier principle to a predictive theory of transition-metal heterogeneous catalysis.Journal of Catalysis, 328:36–42, 2015. Special Issue: The Impact of Haldor Topsøe on Catalysis

    2015

  54. [54]

    Electrified cement production via anion-mediated electrochemical calcium extraction.ACS Energy Letters, 8(11):4694–4701, 2023

    Rui Kai Miao, Ning Wang, Sung-Fu Hung, Wen-Yang Huang, Jinqiang Zhang, Yong Zhao, Pengfei Ou, Sasa Wang, Jonathan P Edwards, Cong Tian, et al. Electrified cement production via anion-mediated electrochemical calcium extraction.ACS Energy Letters, 8(11):4694–4701, 2023

    2023

  55. [55]

    The roles of ions in electrochemical interface for electrocatalysis.ACS Catalysis, 15(10):7780–7791, 2025

    Jongsu Noh, Hanjoo Kim, Hyein Park, and Dong Young Chung. The roles of ions in electrochemical interface for electrocatalysis.ACS Catalysis, 15(10):7780–7791, 2025. 13

    2025

  56. [56]

    J. K. Nørskov, T. Bligaard, J. Rossmeisl, and C. H. Christensen. Towards the computational design of solid catalysts.Nature Chemistry, 1(1):37–46, 2009

    2009

  57. [57]

    Density functional theory in surface chemistry and catalysis.Proceedings of the National Academy of Sciences, 108(3):937–943, 2011

    Jens K Nørskov, Frank Abild-Pedersen, Felix Studt, and Thomas Bligaard. Density functional theory in surface chemistry and catalysis.Proceedings of the National Academy of Sciences, 108(3):937–943, 2011

    2011

  58. [58]

    Application of machine learning interatomic potentials in heterogeneous catalysis.Journal of Catalysis, 448:116202, 2025

    Gbolagade Olajide, Khagendra Baral, Sophia Ezendu, Ademola Soyemi, and Tibor Szilvasi. Application of machine learning interatomic potentials in heterogeneous catalysis.Journal of Catalysis, 448:116202, 2025

    2025

  59. [59]

    Understanding activity trends in electrochemical dinitrogen oxidation over transition metal oxides.ACS Catalysis, 14(22): 16885–16896, 2024

    Samuel A Olusegun, Yancun Qi, Nishithan C Kani, Meenesh R Singh, and Joseph A Gauthier. Understanding activity trends in electrochemical dinitrogen oxidation over transition metal oxides.ACS Catalysis, 14(22): 16885–16896, 2024

    2024

  60. [60]

    Correlating hydration free energy and specific adsorption of alkali metal cations during co2 electroreduction on au.Nature Catalysis, 5 (7):624–632, 2022

    Vincent J Ovalle, Yu-Shen Hsu, Naveen Agrawal, Michael J Janik, and Matthias M Waegele. Correlating hydration free energy and specific adsorption of alkali metal cations during co2 electroreduction on au.Nature Catalysis, 5 (7):624–632, 2022

    2022

  61. [61]

    Proton relay for the rate enhancement of electrochemical hydrogen reactions at heterogeneous interfaces.Journal of the American Chemical Society, 145(48):26016–26027, 2023

    Yang Qiu, Debmalya Ray, Litao Yan, Xiaohong Li, Miao Song, Mark H Engelhard, Junming Sun, Mal-Soon Lee, Xin Zhang, Manh-Thuong Nguyen, et al. Proton relay for the rate enhancement of electrochemical hydrogen reactions at heterogeneous interfaces.Journal of the American Chemical Society, 145(48):26016–26027, 2023

    2023

  62. [62]

    Quantum chemistry structures and properties of 134 kilo molecules.Scientific Data, 1(1):1–7, 2014

    Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules.Scientific Data, 1(1):1–7, 2014

    2014

  63. [63]

    Hörmann, Harald Oberhofer, and Karsten Reuter

    Stefan Ringe, Nicolas G. Hörmann, Harald Oberhofer, and Karsten Reuter. Implicit solvation methods for catalysis at electrified interfaces.Chemical Reviews, 122(12):10777–10820, 2022

    2022

  64. [64]

    Understanding the influence of solvents on the pt-catalyzed hydrodeoxygenation of guaiacol.Journal of Catalysis, 425:212–232, 2023

    Mohammad Saleheen, Osman Mamun, Anand Mohan Verma, Dia Sahsah, and Andreas Heyden. Understanding the influence of solvents on the pt-catalyzed hydrodeoxygenation of guaiacol.Journal of Catalysis, 425:212–232, 2023

    2023

  65. [65]

    Aqcat25 dataset.https://huggingface.co/datasets/SandboxAQ/aqcat25, 2025

    SandboxAQ. Aqcat25 dataset.https://huggingface.co/datasets/SandboxAQ/aqcat25, 2025

    2025

  66. [66]

    Electrification and decarbonization of the chemical industry.Joule, 1(1):10–14, 2017

    Zachary J Schiffer and Karthish Manthiram. Electrification and decarbonization of the chemical industry.Joule, 1(1):10–14, 2017

    2017

  67. [67]

    A review on recent trends and future developments in electrochemical sensing.ACS Omega, 9(7):7336–7356, 2024

    Rimmy Singh, Ruchi Gupta, Deepak Bansal, Rachna Bhateria, and Mona Sharma. A review on recent trends and future developments in electrochemical sensing.ACS Omega, 9(7):7336–7356, 2024

    2024

  68. [68]

    The open dac 2023 dataset and challenges for sorbent discovery in direct air capture.ACS Central Science, 10(5):923–941, 2024

    Anuroop Sriram, Sihoon Choi, Xiaohan Yu, Logan M Brabson, Abhishek Das, Zachary Ulissi, Matt Uyttendaele, Andrew J Medford, and David S Sholl. The open dac 2023 dataset and challenges for sorbent discovery in direct air capture.ACS Central Science, 10(5):923–941, 2024

    2023

  69. [69]

    The open dac 2025 dataset for sorbent discovery in direct air capture.arXiv preprint arXiv:2508.03162, 2025

    Anuroop Sriram, Logan M Brabson, Xiaohan Yu, Sihoon Choi, Kareem Abdelmaqsoud, Elias Moubarak, Pim de Haan, Sindy Löwe, Johann Brehmer, John R Kitchin, et al. The open dac 2025 dataset for sorbent discovery in direct air capture.arXiv preprint arXiv:2508.03162, 2025

    arXiv 2025

  70. [70]

    Improving the accuracy of atomistic simulations of the electrochemical interface.Chemical Reviews, 122(12):10651–10674, 2022

    Ravishankar Sundararaman, Derek Vigil-Fowler, and Kathleen Schwarz. Improving the accuracy of atomistic simulations of the electrochemical interface.Chemical Reviews, 122(12):10651–10674, 2022

    2022

  71. [71]

    Engineering challenges in green hydrogen production systems.Journal of The Electrochemical Society, 169(5):054503, 2022

    Meng Tao, Joseph A Azzolini, Ellen B Stechel, Katherine E Ayers, and Thomas I Valdez. Engineering challenges in green hydrogen production systems.Journal of The Electrochemical Society, 169(5):054503, 2022

    2022

  72. [72]

    Active learning across intermetallics to guide discovery of electrocatalysts for co2 reduction and h2 evolution.Nature Catalysis, 1(9):696–703, 2018

    Kevin Tran and Zachary W Ulissi. Active learning across intermetallics to guide discovery of electrocatalysts for co2 reduction and h2 evolution.Nature Catalysis, 1(9):696–703, 2018

    2018

  73. [73]

    The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts.ACS Catalysis, 13(5):3066–3084, 2023

    Richard Tran, Janice Lan, Muhammed Shuaibi, Brandon M Wood, Siddharth Goyal, Abhishek Das, Javier Heras-Domingo, Adeesh Kolluru, Ammar Rizvi, Nima Shoghi, et al. The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts.ACS Catalysis, 13(5):3066–3084, 2023

    2022

  74. [74]

    Cattsunami: Accelerating transition state energy calculations with pretrained graph neural networks.ACS Catalysis, 15(7): 5283–5294, 2025

    Brook Wander, Muhammed Shuaibi, John R Kitchin, Zachary W Ulissi, and C Lawrence Zitnick. Cattsunami: Accelerating transition state energy calculations with pretrained graph neural networks.ACS Catalysis, 15(7): 5283–5294, 2025

    2025

  75. [75]

    Electrochemical epoxidation of propylene to propylene oxide via halogen-mediated systems.ACS Omega, 8(49):46569–46576, 2023

    Jiangjiang Wang, Gangfeng Wu, Guanghui Feng, Guihua Li, Yiheng Wei, Shoujie Li, Jianing Mao, Xiaohu Liu, Aohui Chen, Yanfang Song, et al. Electrochemical epoxidation of propylene to propylene oxide via halogen-mediated systems.ACS Omega, 8(49):46569–46576, 2023. 14

    2023

  76. [76]

    fast-charging

    Rui Wang, Lu Wang, Rui Liu, Xiangye Li, Youzhi Wu, and Fen Ran. “fast-charging” anode materials for lithium-ion batteries from perspective of ion diffusion in crystal structure.ACS Nano, 18(4):2611–2648, 2024

    2024

  77. [77]

    Catalysis-hub

    Kirsten T Winther, Max J Hoffmann, Jacob R Boes, Osman Mamun, Michal Bajdich, and Thomas Bligaard. Catalysis-hub. org, an open electronic structure database for surface reactions.Scientific Data, 6(1):75, 2019

    2019

  78. [78]

    Uma: A family of universal models for atoms.arXiv preprint arXiv:2506.23971, 2025

    Brandon M Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R Kitchin, Daniel S Levine, et al. Uma: A family of universal models for atoms.arXiv preprint arXiv:2506.23971, 2025

    arXiv 2025

  79. [79]

    Energy decarbonization via green h2 or nh3?ACS Energy Letters, 7(3):1021–1033, 2022

    Simson Wu, Nicholas Salmon, Molly Meng-Jung Li, René Bañares-Alcántara, and Shik Chi Edman Tsang. Energy decarbonization via green h2 or nh3?ACS Energy Letters, 7(3):1021–1033, 2022

    2022

  80. [80]

    Emerging electrochemical processes to decarbonize the chemical industry

    Rong Xia, Sean Overa, and Feng Jiao. Emerging electrochemical processes to decarbonize the chemical industry. JACS Au, 2(5):1054–1070, 2022

    2022

Showing first 80 references.