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REVIEW 4 major objections 6 minor 41 references

Uni-Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper reports the first AI platform, Uni-Electrolyte, that unifies electrolyte molecule generation, retrosynthesis planning, and SEI formation prediction in one iterative design loop.

desk verdict A competent integration of existing AI tools for electrolyte design, but the central generation claim is circular and the platform is not released. read the letter →

arxiv 2412.00498 v1 pith:YYPTOKXQ submitted 2024-11-30 cond-mat.mtrl-sci

Xiang Chen , Mingkang Liu , Shiqiu Yin , Yu-Chen Gao , Nan Yao , Qiang Zhang This is my paper · ORCID
classification cond-mat.mtrl-sci
keywords electrolytedesignrechargeablebatterieslithiumartificialintelligencegenerativemolecularretrosynthesissolidinterphaseQSPR
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 claims that electrolyte discovery does not have to rely on trial and error: a single AI platform can generate candidate solvent and additive molecules, predict their key properties, plan their synthesis, and predict the solid-electrolyte interphase they will form. The authors introduce Uni-Electrolyte, built from three connected modules: EMolCurator for molecular design and screening, EMolForger for retrosynthesis, and EMolNetKnittor for SEI formation analysis. If the platform works as claimed, it would compress a large part of electrolyte research and development into an iterative computational loop that could accelerate next-generation lithium batteries. The paper validates each module on known electrolytes, literature synthesis routes, and a predicted FEC decomposition pathway.

What carries the argument

The load-bearing mechanism is a DFT/MD-trained property oracle embedded in an iterative design loop: EMolCurator's quantitative structure-property relationship models, with G2GT as the best 2D model and LEFTNet as the best 3D model, predict HOMO-LUMO gaps, Li+ binding energy, viscosity, and dielectric constant, and these predictions both steer the generative models and filter the output through stability, novelty, and synthesizability checks. Around this oracle, EMolForger couples a graph-to-graph one-step retrosynthesis predictor with a route planner fine-tuned on electrolyte-like reactions, and EMolNetKnittor extends the reaction-network builder to the full LiBE electrolyte database so a candidate molecule's SEI products can be predicted in the same workflow.

What would settle it

Take molecules generated in the HOMO-LUMO and binding-energy tasks, compute their HOMO-LUMO gaps and Li+ binding energies with the same DFT/MD methodology used to build the training database, and compare against the values predicted by the SOTA models used in the paper. If the predicted and recalculated values disagree by more than the benchmark MAEs reported in the paper for a meaningful fraction of the sample, the property-targeted generation claim is not supported; the complementary check is to synthesize a top-ranked candidate and measure its dielectric constant, viscosity, and reductive stability.

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Extended reading notes

Core claim

The central claim is that the three normally separate stages of electrolyte development can be linked in one platform. EMolCurator uses QSPR models trained on a DFT and MD database to predict HOMO-LUMO energies, binding energy with a Li ion, viscosity, and dielectric constant, and it combines database screening, similarity search, and generative models to propose new molecules; the authors show that conditional diffusion generation can populate sparse HOMO-LUMO regions and even recover a molecule like DME outside the training set. EMolForger replaces a template-based retrosynthesis predictor with the template-free G2GT model and couples it with a route planner, reporting higher Top-1 accuracy (0.529 versus 0.452) and more recalled molecules (17 versus 8) than the standard Askcos pipeline, and reproducing literature routes for fluorinated diethoxyethane derivatives after fine-tuning on electrolyte-like reactions. EMolNetKnittor extends the HiPRGen reaction-network builder to the full LiBE database and predicts that FEC decomposes through a double-coordinated Li-FEC intermediate into LiF and a polymeric SEI component. The paper presents these as the components of the first AI platform covering the whole electrolyte design loop.

Load-bearing premise

The loop works only if the property-prediction models are accurate enough, and the paper evaluates generated molecules with the same pretrained models trained on the same DFT/MD database that guided generation, without independent DFT recalculation or experimental confirmation.

Editorial extensions

If this is right

  • A researcher with a target property window, such as a specific HOMO-LUMO gap or Li+ binding energy, can generate candidate molecules outside existing electrolyte databases and filter them for synthesizability, including molecules in sparse property regions like DME.
  • Retrosynthesis planning becomes specifically adapted to electrolytes: the fine-tuned single-step predictor achieves higher Top-1 accuracy than the generic baseline and can propose literature-consistent routes for fluorinated diethoxyethane derivatives.
  • SEI analysis can cover a wider range of electrolyte chemistries, including molecules containing F, N, P, and S, because the reaction network spans the full LiBE database and can also build custom databases on the fly for new inputs.
  • The three modules form an iterative loop, so a candidate that fails synthesis or SEI criteria can be redesigned with adjusted targets without leaving the platform.

Reading between the lines

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

  • The paper validates each module separately but does not demonstrate the full loop on one molecule: generation, property verification, synthesis planning, and SEI analysis in sequence. A natural test would be to run the entire pipeline on a fresh target and measure the experimental hit rate.
  • Because the generative models and the models used to evaluate them are trained on the same DFT/MD database, the out-of-domain property claims would be strengthened by an independent DFT calculation on generated molecules; the paper does not report such recalculations.
  • The modular architecture could plausibly be rebuilt for sodium, potassium, or zinc batteries, or for electrolyte additives beyond solvents, by replacing the underlying DFT/MD database; the paper only presents lithium electrolyte examples.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The manuscript describes Uni-Electrolyte, an AI platform that integrates three modules: EMolCurator for property prediction, screening, similarity search, and generative molecular design; EMolForger for retrosynthetic analysis; and EMolNetKnittor for SEI formation analysis. The QSPR models are benchmarked on IID and OOD splits, property-targeted generation is demonstrated for HOMO–LUMO gap, binding energy, and fingerprint similarity tasks, retrosynthesis is compared with Askcos on 100 electrolyte molecules, and an FEC decomposition case study is presented. The authors claim this is the first integrated AI platform for electrolyte design.

Significance. The potential significance is real: an integrated, user-facing platform that combines generation, screening, synthesis planning, and SEI analysis would be valuable for electrolyte research. The manuscript includes useful concrete elements: a held-out QSPR benchmark with IID/OOD separation, a retrosynthesis comparison on electrolyte-specific molecules, and an SEI case study consistent with prior literature. However, the central design claim rests on the generative module, whose evaluation is circular and whose property predictions have large OOD errors. If the authors provide independent validation, the platform would be a meaningful contribution; as it stands, the evidence is suggestive but not conclusive.

major comments (4)
  1. [3.3] The evaluation of property-targeted generation is circular: the generated molecules are scored with the same 'SOTA pre-trained models' that were trained on the same DFT&MD dataset used to train and guide the generative models (Secs. 2.2.1 and 2.2.4). This only shows that the generator can match the predictor; it does not establish that the generated molecules actually have the requested HOMO–LUMO gaps, binding energies, or other properties. The authors should validate a sample of generated molecules by independent DFT/MD calculations or experiments, and should quantify how sensitive the generation results are to the QSPR model errors reported in Table 1. Without such validation, the claim that Uni-Electrolyte 'designs' molecules with targeted properties is not supported.
  2. [Table 1] The OOD MAEs for viscosity (13.0–14.9 mPa·s across models) and dielectric constant (3.3–3.7) are comparable to or larger than the example values used in the similarity query in Sec. 3.2 (η = 5.5 mPa·s, ε = 1.34). Because these QSPR predictions drive both the screening filters and the property-targeted generation loop, the large OOD errors imply that the design workflow can be misdirected for novel scaffolds. The paper should provide calibration plots or confidence intervals for the QSPR predictions, and should report how many generated molecules remain after filtering when error bars are taken into account.
  3. [3.3 and Figure 7] There is a factual inconsistency in the flagship example: the text states that the generated molecules 'successfully include DME,' while the Figure 7 caption says the training set 'does not contain the DMC molecule' and the generated dataset 'has the DMC molecule.' DME (1,2-dimethoxyethane) and DMC (dimethyl carbonate) are different molecules. The authors must correct this and clarify whether DME or DMC was actually generated, and how its absence from the training set was verified.
  4. [2.1 (and throughout)] The manuscript does not include a data and code availability statement, and the platform is not released. Since the central claim is the introduction of a new AI platform, the absence of any public access to code, models, or datasets prevents independent verification and adoption. At a minimum, the authors should state the intended release status and provide a detailed description of the implementation, or include a link to a repository in the revised manuscript.
minor comments (6)
  1. [1] Typographical errors: 'Nobel prices' should be 'Nobel Prizes' and 'filed' should be 'field'.
  2. [Table 1] The 'Relative error' row at the bottom of Table 1 is undefined; specify how it is computed (e.g., mean relative error across all properties).
  3. [3.4] The 'Number of Molecules Recalled' for Askcos (8) and G2GT (17) is quite small relative to the 100-molecule test set; the authors should explain whether this is top-5 recall and discuss the implications for practical synthesis planning.
  4. [Figure 6 caption] The sentence 'The number of bottom the molecules is property vector ordered as...' is garbled; it should be rewritten, e.g., 'The property vectors below each molecule are ordered as...'.
  5. [3.3] The abbreviation EDM is expanded as 'Energy-Based Diffusion Model,' but reference [37] (Hoogeboom et al.) describes it as an Equivariant Diffusion Model; correct the terminology.
  6. [2.4 and 3.5] The relationship between the EMolNetKnittor module and the xHiPRGen software is unclear; clarify whether xHiPRGen is the implementation of EMolNetKnittor or a separate tool used inside it.

Circularity Check

1 steps flagged · score 6.0 of 10

Property-targeted generation is validated only by SOTA QSPR predictors trained on the same DFT/MD database as the generator, so the reported 'out-of-domain performance' is a self-consistency check rather than an external validation.

  1. fitted input called prediction [Section 3.3 (Property-targeted generation results); cf. Sections 2.2.1 and 2.2.4]
    "For property-targeted tasks (Tasks 1 and 2), state-of-the-art (SOTA) pre-trained models are utilized to predict the targeted properties, and the distribution of these properties is compared to that of the training set."

    The generative module is explicitly trained on the same DFT&MD dataset as the QSPR evaluator: Section 2.2.4 says 'This module, trained on the same DFT and MD dataset, can generate novel molecules with targeted properties,' and Section 2.2.1 says the QSPR model is 'trained on a comprehensive electrolyte dataset constructed from DFT and MD calculations.' In Section 3.3 the only evidence that generated molecules have the targeted properties is the prediction of 'SOTA pre-trained models,' i.e., QSPR models trained on that same database. Agreement between the generator and this evaluator may reflect shared systematic bias rather than actual molecular properties, and no independent DFT/MD recalculation or experiment on the generated molecules is reported.

full rationale

The paper's QSPR benchmark (Table 1) is a legitimate external evaluation of the property predictors against held-out DFT/MD data, so those predictors themselves are not circularly validated. However, the property-targeted generation results in Section 3.3 are evaluated by comparing the training-set distribution with properties of generated molecules predicted by 'SOTA pre-trained models.' The generative module is explicitly 'trained on the same DFT and MD dataset' (Section 2.2.4) that produced the QSPR training labels (Section 2.2.1). Therefore the generator and the evaluator share the same fitted labels; a generated molecule's property is never recomputed by DFT/MD or measured in the lab. The observed 'concentrated distribution' around the target gap therefore demonstrates only that the generator can produce molecules that the shared surrogate scores as on-target, not that the molecules actually have the target HOMO-LUMO gap or binding energy. This is a partial circularity: the design claim rests on model self-consistency, while the underlying QSPR models retain independent benchmark support. The high OOD MAEs in Table 1 (e.g., viscosity 13.61 mPa·s) further weaken the surrogate-as-ground-truth move, but that is a performance concern; the structural circularity is the shared database between generator and evaluator. No other load-bearing step reduces to a fit or self-citation, so the score is 6 rather than higher.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No genuinely new physical entities are introduced. The load-bearing assumptions are that simulation-derived labels are accurate, that learned predictors can validate their own generative outputs, and that general organic reaction data transfers to electrolytes. These assumptions are not independently tested in the paper.

free parameters (3)
  • QSPR model weights and hyperparameters = not disclosed
    All property predictions, similarity queries, and generation-evaluation loops depend on these fitted parameters; no weights or hyperparameter tables are released.
  • Screening thresholds = formation energy < 0 eV/atom; RAScore > 0.9 (database), RAScore > 0.8 (generated)
    Chosen by hand in Sections 2.2.4 and 3.2; no sensitivity analysis is given, and these cutoffs determine which molecules enter the database and survive generation.
  • Reaction subset size and similarity cutoffs for retrosynthesis fine-tuning = 1,500 reactions; minimum product similarity 0.53; average 0.65
    Section 3.4; the choice of subset and cutoffs affects G2GT's electrolyte-domain adaptation, with no ablation demonstrating robustness.
assumptions (4)
  • domain assumption The DFT/MD database provides accurate ground-truth values for HOMO, LUMO, binding energy, viscosity, and dielectric constant.
    Invoked in Sections 2.2.1 and 3.1 as the training target; if the simulations are inaccurate, all downstream predictions inherit the error.
  • ad hoc to paper The learned property predictors are reliable enough to serve as the evaluation metric for generated molecules.
    Section 3.3 uses SOTA pre-trained models to judge whether generated molecules meet target properties, without independent recalculation or experiments; this is a self-referential validation loop.
  • domain assumption RAScore is a valid proxy for synthesizability of electrolyte molecules.
    Used in Sections 2.2.4 and 3.2 to filter candidates; RAScore was trained on general organic chemistry and may not capture electrolyte-specific synthesis difficulty.
  • domain assumption Reaction data from USPTO and Reaxys transfer to electrolyte chemistry after similarity-based fine-tuning.
    Section 3.4 assumes 1,500 electrolyte-similar reactions are enough to adapt G2GT; validation is limited to four fluorinated DEE molecules.

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Cite this review

Pith. "Pith review of Uni-Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries." pith.science (2026). https://pith.science/paper/YYPTOKXQ

@misc{pith2026241200498,
  author       = {Pith},
  title        = {Pith review of: Uni-Electrolyte: An Artificial Intelligence Platform for Designing Electrolyte Molecules for Rechargeable Batteries},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YYPTOKXQ}},
  note         = {Machine review of arXiv:2412.00498}
}
read the original abstract

Electrolyte is a very important part of rechargeable batteries such as lithium batteries. However, the electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (>1060 for small molecules). This work reported an artificial intelligence (AI) platform, namely Uni-Electrolyte, for designing advanced electrolyte molecules, which mainly includes three parts, i.e. EMolCurator, EMolForger, and EMolNetKnittor. New molecules can be designed by combining high-throughput screening and generative AI models from more than 100 million alternative molecules in the EMolCurator module. The molecule properties including frontier molecular orbital information, formation energy, binding energy with a Li ion, viscosity, and dielectric constant, can be adopted as the screening parameters. The EMolForger, and EMolNetKnittor module can predict the retrosynthesis pathway and reaction pathway with electrodes for a given molecule, respectively. With the assist of advanced AI methods, the Uni-Electrolyte is strongly supposed to discover new electrolyte molecules and chemical principles, promoting the practical application of next-generation rechargeable batteries.

Figures

Figures reproduced from arXiv: 2412.00498 by the authors.

Figure 1
Figure 1. Schematic representation of the Uni-Electrolyte platform with three modules. The 'EMolCurator' module aims to design new electrolyte molecules. Based embedded electrolyte database, QSPR and AI-based generative models were trained. The 'EMolForger' module can predict the synthesis pathways and corresponding reaction conditions of potential electrolyte molecules. It was built with a synthetic route planner and AI-base… view at source ↗
Figure 2
Figure 2. Three functions of the EMolCurator module. (a) The QSPR model is benchmarked and trained on the DFT and MD databases. It intakes 2D or 3D molecular graphs and outputs their properties. (b) Pre-designed candidate electrolyte molecules are screened with respect to user-defined intervals. (c) Query similar-properties molecules with the vector database, the queried vector itself is composed of the predicted properties f… view at source ↗
Figure 3
Figure 3. The AI-driven molecule generation workflow. Benchmarked on DFT & MD databases, the SOTA model can be guided with user-defined targets. An automated cleaning pipeline is built to identify stable, unique and synthesizable molecules. The entire workflow can be iterated until convergence is reached. 2.3 EMolForger: AI-Powered Retrosynthetic Analysis Module Retrosynthetic analysis is a critical bridge between theoretical… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: The illustration of the retrosynthesis module. The module includes two AI components, i.e. the G2GT one-step retrosynthesis predictor and the Askcos synthetic route planner. During inference, the module proposes purchasable starting reagents and potential intermediates…
Figure 5
Figure 5. Figure 5: Illustration of the SEI-analysis module. The input electrolyte molecule is queried against a built-in database at first. If related species and reactions are found, this module utilizes stochastic kMC simulations to build a reaction network. This network then allows th…
Figure 8
Figure 8. Figure 8: The retrosynthesis results of (a) F6DEE and F3DEE, (b) F4DEE and F5DEE are shown above. The proposed reagents are denoted above the reaction arrow, while the reaction conditions are labeled underneath [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]
Figure 9
Figure 9. Figure 9: The proposed FEC decomposition route to potential SEI product. The Gibbs free energy of each reaction is denoted on top of the reaction arrow. 4. Conclusions An AI platform, namely the Uni-Electrolyte, has been developed to design battery electrolyte molecules based on…

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Reference graph

Works this paper leans on

41 extracted references · 38 canonical work pages

  1. [1]

    N. Yao, X. Chen, Z. -H. Fu, Q. Zhang, Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics S imulations to the Liquid Electrolyte for Rechargeable Batteries. Chem. Rev. 122 (2022) 10970–11021

    work page 2022

  2. [2]

    X. Chen, Q. Zhang, Atomic Insights into the Fundamental Interactions in Lithium Battery Electrolytes. Acc. Chem. Res. 53 (2020) 1992–2002

    work page 2020

  3. [3]

    Zhang, W

    J.-G. Zhang, W. Xu, J. Xiao, X. Cao, J. Liu, Lithium Metal Anodes with Nonaqueous Electrolytes. Chem. Rev. 120 (2020) 13312–13348

    work page 2020

  4. [4]

    X. Fan, C. Wang, High -V oltage Liquid Electrolytes for Li Batteries: Progress and Perspectives. Chem. Soc. Rev. 50 (2021) 10486–10566

    work page 2021

  5. [5]

    Yamada, J

    Y . Yamada, J. Wang, S. Ko, E. Watanabe, A. Yamada, Advances and Issues in Developing Salt-Concentrated Battery Electrolytes. Nat. Energy 4 (2019) 269– 280

    work page 2019

  6. [6]

    Winter, B

    M. Winter, B. Barnett, K. Xu, Before Li Ion Batteries. Chem. Rev. 118 (2018) 11433–11456

    work page 2018

  7. [7]

    Xu, Electrolytes and Interphases in Li-Ion Batteries and Beyond

    K. Xu, Electrolytes and Interphases in Li-Ion Batteries and Beyond. Chem. Rev. 114 (2014) 11503–11618

    work page 2014

  8. [8]

    Xu, Nonaqueous Liquid Electrolytes for Lithium -Based Rechargeable Batteries

    K. Xu, Nonaqueous Liquid Electrolytes for Lithium -Based Rechargeable Batteries. Chem. Rev. 104 (2004) 4303–4417

    work page 2004

Show all 41 references
  1. [9]

    Cheng, R

    X.-B. Cheng, R. Zh ang, C. -Z. Zhao, Q. Zhang, Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review. Chem. Rev. 117 (2017) 10403 – 10473

    2017

  2. [10]

    X. He, D. Bresser, S. Passerini, F. Baakes, U. Krewer, J. Lopez, C. T. Mallia, Y . Shao-Horn, I. Cekic-Laskovic, S. Wiemers-Meyer, F. A. Soto, V . Ponce, J. M. Seminario, P. B. Balbuena, H. Jia, W. Xu, Y . Xu, C. Wang, B. Horstmann, R. Amine, C. -C. Su, J. Shi, K. Amine, M. Wi...

    2021

  3. [11]

    H. Wang, Z. Yu, X. Kong, S. C. Kim, D. T. Boyle, J. Qin, Z. Bao, Y . Cui, Liquid Electrolyte: The Nexus of Practical Lithium Metal Batteries. Joule 6 (2022) 588–616

    2022

  4. [12]

    X. Yang, Y . Wang, R. Byrne, G. Schn eider, S. Yang, Concepts of Artificial 25 Intelligence for Computer -Assisted Drug Discovery. Chem. Rev. 119 (2019) 10520–10594

    2019

  5. [13]

    K. M. Jablonka, D. Ongari, S. M. Moosavi, B. Smit, Big-Data Science in Porous Materials: Materials Genomics and Machine Learning. Chem. Rev. 120 (2020) 8066–8129

    2020

  6. [14]

    Lombardo, M

    T. Lombardo, M. Duquesnoy, H. El -Bouysidy, F. Årén, A. Gallo-Bueno, P. B. Jørgensen, A. Bhowmik, A. Demortière, E. Ayerbe, F. Alcaide, M. Reynaud, J. Carrasco, A. Grimaud, C. Zhang, T. Vegge, P. Johansson, A. A. Franco, Artificial Intelligence Applied to Battery Research: Hyp...

    2021

  7. [15]

    P. M. Attia, A. Grover, N. Jin, K. A. Severson, T. M. Markov, Y .-H. Liao, M. H. Chen, B. Cheong, N. Perkins, Z. Yang, P. K. Herring, M. Aykol, S. J. Harris, R. D. Braatz, S. Ermon, W. C. Chueh, Closed-Loop Optimization of Fast-Charging Protocols for Batteries with Machine Lea...

    2020

  8. [16]

    K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, A. Walsh, Machine Learning for Molecular and Materials Science. Nature 559 (2018) 547–555

    2018

  9. [17]

    B. J. Shields, J. Stevens, J. Li, M. Parasram, F. Damani, J. I. M. Alvarado, J. M. Janey, R. P. Adams, A. G. Doyle, Bayesian Reaction Optimization as a Tool for Chemical Synthesis. Nature 590 (2021) 89–96

    2021

  10. [18]

    Magdău, D

    I.-B. Magdău, D. J. Arismendi-Arrieta, H. E. Smith, C. P. Grey, K. Hermansson, G. Csányi, Machine Learning Force Fields for Molecular Liquids: Ethylene Carbonate/Ethyl Methyl Carbonate Binary Solvent. npj Comp. Mater. 9 (2023) 146

    2023

  11. [19]

    Dajnowicz, G

    S. Dajnowicz, G. Agarwal, J. M. Stevenson, L. D. Jacobson, F. Ramezanghorbani, K. Leswing, R. A. Friesner, M. D. Halls, R. Abel, High - Dimensional Neural Network Potential for Liquid Electrolyte Simulations. J. Phys. Chem. B 126 (2022) 6271–6280

    2022

  12. [20]

    S. Gong, Y . Zhang, Z.-H. Mu, Z. Pu, H. Wang, Z. Yu, M. Chen, T. Zheng, Z. Wang, L. Chen, X. Wu, S. Shi, W. Gao, W. Yan, L. Xiang, BAMBOO: A Predictive and Transferable Machine Learning Force Field Framework for Liquid Electrolyte Development. ArXiv abs/2404.07181 (2024)

    2024 arXiv

  13. [21]

    Gao, Y .-H

    Y.-C. Gao, Y .-H. Yuan, S. Huang, N. Yao, L. Yu, Y .-P. Chen, Q. Zhang, X. Chen, A Knowledge –Data Dual -Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes. Angew. Chem. Int. Ed. (2024) e202416506

    2024

  14. [22]

    Y.-C. Gao, N. Yao, X. Chen, L. Yu, R. Zhang, Q. Zhang, Data -Driven Insight into the Reductive Stability of Ion –Solvent Complexes in Lithium Battery Electrolytes. J. Am. Chem. Soc. 145 (2023) 23764–23770

    2023

  15. [23]

    Z. Lin, S. Yin, L. Shi, W. Zhou, Y . J. Zhang, G2GT: Retrosynthesis Prediction with Graph-to-Graph Attention Neural Network and Self -Training. Journal of Chemical Information and Modeling 63 (2023) 1894–1905

    2023

  16. [24]

    C. W. Coley, D. A. Thomas, J. A. M. Lummiss, J. N. Jaworski, C. P. Breen, V . Schultz, T. Hart, J. S. Fishman, L. Rogers, H. Gao, R. W. Hicklin, P. P. Plehiers, J. Byington, J. S. Piotti, W. H. Green, A. J. Hart, T. F. Jamison, K. F. Jensen, A 26 Robotic Platform for Flow Synt...

    2019

  17. [25]

    Barter, E

    D. Barter, E. W. Clark Spotte-Smith, N. S. Redkar, A. Khanwale, S. Dwaraknath, K. A. Persson, S. M. Blau, Predictive Stochastic Analysis of Massive Filter - Based Electrochemical Reaction Networks. Digital Discovery 2 (2023) 123 – 137

    2023

  18. [26]

    E. W. C. Spotte-Smith, S. M. Blau, X. Xie, H. D. Patel, M. Wen, B. Wood, S. Dwaraknath, K. A. Persson, Quantum Chemical Calculations of Lithium -Ion Battery Electrolyte and Interphase Species. Scientific Data 8 (2021) 203

    2021

  19. [27]

    G. W. Bemis, M. A. Murcko, The Properties of Known Drugs. 1. Molecular Frameworks. J. Med. Chem. 39 (1996) 2887–2893

    1996

  20. [28]

    Schneider, N

    N. Schneider, N. Stiefl, G. A. Landrum, What’s What: The (Nearly) Definitive Guide to Reaction Role Assignment. Journal of Chemical Information and Modeling 56 (2016) 2336–2346

    2016

  21. [29]

    G. Zhou, Z. Gao, Q. Ding, H. Zheng, H. Xu, Z. Wei, L. Zhang, G. Ke In Uni- Mol: A Universal 3D Molecular Representation Learning Framework , International Conference on Learning Representations, 2023

    2023

  22. [30]

    Ramakrishnan, P

    R. Ramakrishnan, P. O. Dral, M. Rupp, O. A. von Lilienfeld, Quantum Chemistry Structures and Properties of 134 Kilo Molecules. Scientific Data 1 (2014) 140022

    2014

  23. [31]

    W. Du, Y . Du, L. Wang, D. Feng, G. Wang, S. Ji, C. P. Gomes, Z. Ma, A New Perspective on Building Efficient and Expressive 3d Equivariant Graph Neural Networks. ArXiv abs/2304.04757 (2023)

    2023 arXiv

  24. [32]

    K. T. Schütt, O. T. Unke, M. Gastegger In Equivariant Message Passing for The Prediction of Tensorial Properties and Molecular Spectra , International Conference on Machine Learning, 2021

    2021

  25. [33]

    K. T. Schütt, H. E. Sauceda, P. -J. Kindermans, A. Tkatchenko, K. -R. Müller, SchNet – A Deep Learning Architecture for Molecules and Materials. J. Chem. Phys. 148 (2018)

    2018

  26. [34]

    Y . Liu, L. Wang, M. Liu, X. Zhang, B. Oztekin, S. Ji In Spherical Message Passing for 3D Molecular Graphs , International Conference on Learning Representations, 2021

    2021

  27. [35]

    Thakkar, V

    A. Thakkar, V . Chadimová, E. J. Bjerrum, O. Engkvist, J. -L. Reymond, Retrosynthetic Accessibility Score (RAscore) – Rapid Machine L earned Synthesizability Classification from AI Driven Retrosynthetic Planning. Chem Sci 12 (2021) 3339–3349

    2021

  28. [36]

    Bajusz, A

    D. Bajusz, A. Rácz, K. Héberger, Why is Tanimoto Index an Appropriate Choice for Fingerprint-Based Similarity Calculations? Journal of Cheminf ormatics 7 (2015) 20

    2015

  29. [37]

    Hoogeboom, V

    E. Hoogeboom, V . G. Satorras, C. Vignac, M. Welling, Equivariant Diffusion for Molecule Generation in 3D. ArXiv abs/2203.17003 (2022)

    2022 arXiv

  30. [38]

    N. W. A. Gebauer, M. Gastegger, S. S. P. Hessmann, K.-R. Müller, K. T. Schütt, Inverse Design of 3d Molecular Structures with Conditional Generative Neural Networks. Nat. Commun. 13 (2022) 973. 27

    2022

  31. [39]

    Rogers, M

    D. Rogers, M. Hahn, Extended-Connectivity Fingerprints. Journal of chemical information and modeling 50 5 (2010) 742–754

    2010

  32. [40]

    Z. Yu, P. E. Rudnicki, Z. Zhang, Z. Huang, H. Celik, S. T. Oyakhire, Y . Chen, X. Kong, S. C. Kim, X. Xiao, H. Wang, Y . Zheng, G. A. Kamat, M. S. Kim, S. F. Bent, J. Qin, Y . Cui, Z. Bao, Rational Solvent Molecule Tuning for High - Performance Lithium Metal Battery Electrolyt...

    2022

  33. [41]

    S. M. Blau, H. D. Patel, E. W. C. Spotte -Smith, X. Xie, S. Dwaraknath, K. Persson, A Chemically Consistent Graph Architecture for Massive Reaction Networks Applied to Solid -Electrolyte Interphase Formation. Chem Sci 12 (2021) 4931–4939

    2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.