Pith. sign in

REVIEW 3 major objections 5 minor 59 references

Category-Specific Topological Learning of Metal-Organic Frameworks

T0 review · 3 major / 5 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 By grouping atoms into chemical categories before computing persistent homology, CSTL predicts eight MOF gas properties with R2 up to 0.85, beating transformer models trained on millions of structures.

desk verdict A useful descriptor extension, but the SOTA claim rests on datasets and splits that don't match the baselines; needs a controlled re-benchmark and released code before I'd trust the headline. read the letter →

arxiv 2412.11386 v1 pith:N2LL36J4 submitted 2024-12-16 q-bio.BM cond-mat.mtrl-sciphysics.comp-ph

Dong Chen , Chun-Long Chen , Guo-Wei Wei This is my paper · ORCID
classification q-bio.BMcond-mat.mtrl-sciphysics.comp-ph MSC 55N3168T05
keywords metal-organicframeworkspersistenthomologytopologicaldataanalysiscategory-specificdescriptorsgasselectivitymachinelearningpropertypredictiongradientboostingalphacomplex
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

This paper introduces category-specific topological learning (CSTL), a descriptor method that predicts eight N2/O2-related properties of metal-organic frameworks from crystal structure alone. It represents each MOF as a simplicial complex, splits atoms into eight chemically meaningful element categories plus an all-atom category, and runs persistent homology on each category to produce barcode-derived feature vectors. A gradient boosting model on these 6750-dimensional descriptors reaches R2 values between 0.79 and 0.85 across the eight datasets, exceeding the descriptor-based, MOFTransformer, and PMTransformer baselines. The authors argue the method is interpretable: feature importance traces gas selectivity to carbon cavities and metal-node influence. If correct, CSTL offers a lighter, explainable route to accurate MOF property screening without pretraining on millions of structures.

What carries the argument

The central object is category-specific persistent homology on alpha complexes. An alpha complex is a simplicial complex grown from the Delaunay triangulation of atomic positions as a radius parameter increases; persistent homology tracks when connected components, loops, and cavities appear and disappear, summarized as barcodes. The paper's twist is to compute these barcodes separately for eight element categories (alkali and other metals C0, transition/lanthanide/actinide metals C1, metalloids C2, halogens C3, hydrogen C4, carbon C5, nitrogen/phosphorus C6, oxygen/sulfur/selenium C7) and for all atoms together. Binning each barcode over 0 to 25 angstroms with 0.1 angstrom steps produces a length-fixed vector, and concatenation gives a 6750-dimensional descriptor that a gradient boosting tree model maps to each target property. The categories make the topology chemically aware and keep rare elements from being swamped by abundant carbon and oxygen.

What would settle it

Retrain the descriptor-based model, MOFTransformer, and PMTransformer on the exact eight filtered datasets and 80/10/10 splits used for CSTL; if any baseline matches or exceeds its R2 on the same test folds, the central outperformance claim is refuted.

Watch

Extended reading notes

Core claim

The central claim is that category-specific persistent homology, rather than more data or deeper models, carries the predictive signal for MOF gas properties. For each elemental category C0 through C7 and the whole structure Call, the method builds an alpha complex filtration and records Betti numbers of H0, H1, and H2 on a 0 to 25 angstrom grid, yielding 750 features per category and 6750 features in total. Feeding these descriptors to a gradient boosting regressor gives test R2 values between 0.79 and 0.85 on eight datasets covering Henry constants, uptake, and self-diffusivity of N2 and O2, with lower MAE and RMSE than all compared prior models, using one fixed hyperparameter set and 100 repeated splits. The paper also shows t-SNE plots where CSTL features separate MOFs with extreme property values, and tree-based feature importance that assigns physical meaning to the categories.

Load-bearing premise

The claim of outperforming prior models assumes the comparison is fair: the baselines must be evaluated on the same filtered data and same train/test splits, otherwise differences in dataset size or composition, not the descriptors, could explain the accuracy gap.

Editorial extensions

If this is right

  • On the eight N2/O2 datasets, CSTL reports higher R2 and lower MAE/RMSE than the descriptor-based model, MOFTransformer, and PMTransformer, so it would become the benchmark to beat for these properties.
  • Because the descriptors come only from CIF structure files and a fixed hyperparameter set, CSTL can be applied to new MOF datasets without pretraining or fine-tuning.
  • The feature-importance analysis links specific categories to physics: carbon cycles and oxygen/sulfur spacing drive gas selectivity, while loops and cavities in the all-atom complex drive diffusivity.
  • The reported stability across 100 random splits and a 20 percent holdout suggests the accuracy gain is not an artifact of one lucky train/test partition.

Reading between the lines

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

  • If the element-category assignment is what supplies the chemical inductive bias, then coarsening the categories or randomizing the grouping should measurably lower R2; the paper does not run this ablation, but it is directly testable.
  • Because CSTL needs no pretraining corpus, it could serve as a computationally cheap screening baseline for hypothetical MOF libraries, where transformer models trained on existing structures may transfer poorly.
  • The comparison with prior models rests on datasets of slightly different sizes and unreported splits; a strict head-to-head with baselines retrained on CSTL's exact splits would reveal how much of the gap is the descriptor rather than data handling.
  • The fixed 0.1 angstrom binning and 25 angstrom cutoff assume structure-property signal lives in that range; properties governed by finer or longer-range features could escape the descriptor.
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

3 major / 5 minor

Summary. This paper introduces a category-specific topological learning (CSTL) pipeline for predicting eight N2/O2-related properties of metal-organic frameworks. The method constructs alpha complexes from MOF crystal structures, groups elements into eight chemically motivated categories (C0-C7) plus an all-atom set, computes persistent homology in dimensions 0-2 for each category, bins the resulting barcodes on a 0-25 Å grid with 0.1 Å resolution, and concatenates the bin counts into a 6750-dimensional feature vector. A gradient boosting tree regressor with fixed hyperparameters is trained on 80% of each dataset, and metrics are averaged over 100 train/model repetitions. The authors report R2 values between 0.79 and 0.85 across the eight datasets and claim that CSTL outperforms a descriptor-based model, MOFTransformer, and PMTransformer. The paper also presents t-SNE visualizations and tree-based feature importance analyses to support interpretability.

Significance. If the comparison to prior models were controlled, CSTL would be a valuable contribution: it is a compact, interpretable, descriptor-based alternative to large pretrained transformers, it requires no external pretraining corpus, and its descriptors are derived transparently from crystal structure topology rather than from learned representations. The element-category construction is chemically meaningful, and the feature importance analysis gives plausible physical interpretation (e.g., carbon-based cavities for uptake, overall cycles/cavities for diffusivity). I also found no target leakage in the descriptor construction: the element categories are based on elemental occurrence frequencies, not on the labels. However, the headline claim of state-of-the-art performance currently rests on comparisons with datasets of different sizes/filtering and without uncertainty quantification, so the significance of the empirical advantage is not yet established.

major comments (3)
  1. [Section 3.1, Appendix B, Table 4] The central claim that CSTL 'outperforms all previous results' is not established because the benchmark comparisons are not controlled. The CSTL datasets are smaller than those used by MOFTransformer and PMTransformer (e.g., 5132 vs 5286 samples for N2 uptake and 5241 vs 5286 for O2 uptake), the outlier filtering differs, and the exact train/validation/test splits of the baselines are not reproduced or re-run on the CSTL splits. Since the reported R2 advantages are small (0.79 vs 0.78 for N2 uptake; 0.85 vs 0.83 for O2 uptake), differences in dataset composition or split could account for the margin. The authors acknowledge the size discrepancy in Appendix B but do not remediate it. I request a controlled evaluation on identical data and splits, or at least results on both dataset versions and an explicit discussion of how filtering affects each model.
  2. [Table 2, Section 2.2] The text states that CSTL 'consistently outperforms these models across all datasets,' but for four of the eight datasets (Henry's constants for N2/O2 and self-diffusivities at infinite dilution for N2/O2) the MOFTransformer and PMTransformer columns are blank, so there is no comparison to support the claim on those datasets. Either fill in these entries with values from the original papers or by re-running the baselines, or restrict the claim to the datasets for which comparisons exist.
  3. [Appendix C, Table 2] The reported metrics are means over 100 models, but no standard deviations, confidence intervals, or significance tests are reported. The heatmaps in Figures S2-S4 are presented instead of quantitative uncertainty estimates. Given the small R2 differences in Table 2 and the fact that the models are trained on the same data with different seeds, the paper should report the spread of the 100 metrics (e.g., mean ± std or bootstrap CIs) to show whether the improvement over baselines is statistically meaningful.
minor comments (5)
  1. [Section 3.1] The sentence 'The properties of interest, such as O2 and N2 selectivity, were simulated in earlier studies [25]' cites the CoRE MOF database reference [25]; the simulations are from Orhan et al. [27], which is cited later in the same paragraph. Please correct the citation.
  2. [Section 3.2] The phrase 'topological representations are constructed to capture the interactions among atoms across different categories' is imprecise, because each C_i sub-complex contains atoms of a single category; cross-category interactions enter only through the C_all complex. Please rephrase.
  3. [Table 2] The table mixes available metrics across methods (CSTL has r2/MAE/RMSE; Descriptor-based has r2/RMSE; MOFTransformer has r2/MAE; PMTransformer has only MAE) and leaves blanks. Clarify which metrics are available and move the full comparison to a supplementary table if needed.
  4. [Appendix E, Table 5] The column header 'CSTL(80% training, 20% test)' conflicts with the 80:10:10 split described in Section 3.3; if the second column is the 80% training / 20% test+validation holdout, say so explicitly.
  5. [Data availability] The data availability section points to the source repository for the datasets but does not provide code or exact preprocessing scripts for the CSTL descriptors; providing these would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: CSTL descriptors are computed from CIF structures only, targets come from prior simulations, and the claimed outperformance is an external benchmark comparison; dataset-size discrepancies are a fairness concern, not a circular derivation.

full rationale

The paper's derivation chain is self-contained: structure-derived descriptors are built from alpha-complex persistent homology on category-specific atomic subsets (Section 3.2), and the predicted targets are simulated properties from earlier work (Section 3.1, "The properties of interest, such as O2 and N2 selectivity, were simulated in earlier studies [25]"). The input features are explicitly stated to come solely from CIF structural data (Section 3.1: "the input features were derived solely from MOF structural data stored in CIF files, without the use of any additional descriptors"). The element categories in Table 1 are defined by valence-electron similarity and occurrence frequency in the feature distribution, not by the target labels, so no label information is smuggled into the descriptors. The predictive model is a standard gradient-boosting regressor with fixed, untuned hyperparameters (Section 3.3), and no fitted constant is later renamed as a prediction. The claim of outperforming prior models rests on the external comparisons in Table 2; even though Appendix B and Table 4 show that dataset sizes differ slightly from those used by MOFTransformer and PMTransformer, that is a benchmarking-fairness or statistical-significance issue, not circularity, because the CSTL metrics are not algebraically derived from the baseline metrics. Self-citations to prior topological methods (e.g., element-specific persistent homology [39]) are methodological background and are not invoked as a uniqueness theorem or as the source of the target values; they do not make the central derivation reduce to its own inputs. No equation in the paper is equivalent by construction to the quantity it claims to predict. Therefore the circularity score is 0.

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

The central claim depends on several hand-chosen modeling decisions (element categories, filtration range, supercell scaling, GBT hyperparameters) and on domain assumptions about the informativeness of topological features for MOF properties. No new physical entities are postulated. The ledger is modest for an applied ML paper, but the unoptimized choices could materially affect the reported performance.

free parameters (4)
  • Element categories C0-C7
    Elements are grouped into eight categories based on valence electron similarity and occurrence frequency (Table 1). This hand-chosen discretization affects the feature vector and model performance.
  • Filtration distance range and step = 0 to 25 Å, step 0.1 Å
    Persistent homology barcodes are binned into 250 intervals per homology dimension; the range is chosen as 'intentionally large' (Section 2.3) and is not optimized.
  • Supercell scaling target = approximately 64 Å x 64 Å x 64 Å
    Structures are uniformly scaled to this size before topological analysis to ensure consistency, which may affect the multiscale features.
  • GBT hyperparameters = max depth=7, max features='sqrt', min samples leaf=1, min samples split=2, n estimators=10000, subsample=0.5
    A universal set, stated as not fine-tuned, but still fixed choices that influence results.
assumptions (4)
  • domain assumption Persistent homology features are predictive of gas adsorption and diffusion properties of MOFs.
    The entire method rests on the hypothesis that Betti-number curves from alpha complexes encode structure-property relationships; this is supported only by the empirical results.
  • domain assumption The alpha complex built from atomic positions on a scaled supercell captures the relevant porosity and connectivity.
    Section 3.2 constructs alpha complexes from CIF structures; no validation is provided that this representation retains all chemically relevant features.
  • domain assumption The dataset labels (simulated Henry's constants, uptake, diffusivities) from prior studies are accurate enough for benchmarking.
    Data availability states properties were obtained using methods in Orhan et al. [27]; the paper does not independently verify these values.
  • standard math Standard persistent homology stability theorems hold for the feature binning procedure.
    The method relies on established properties of persistent homology (for example, stability), which are not proved in the paper but are standard.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Category-Specific Topological Learning of Metal-Organic Frameworks." pith.science (2026). https://pith.science/paper/N2LL36J4

@misc{pith2026241211386,
  author       = {Pith},
  title        = {Pith review of: Category-Specific Topological Learning of Metal-Organic Frameworks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N2LL36J4}},
  note         = {Machine review of arXiv:2412.11386}
}
read the original abstract

Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational methods have limited scalability and interpretability, hindering effective exploration of MOF structure-property relationships. To address these challenges, we introduce, for the first time, a category-specific topological learning (CSTL), which combines algebraic topology with chemical insights for robust property prediction. The model represents MOF structures as simplicial complexes and incorporates elemental categorizations to enable balanced, interpretable machine learning study. By integrating category-specific persistent homology, CSTL captures both global and local structural characteristics, rendering multi-dimensional, category-specific descriptors that support a predictive model with high accuracy and robustness across eight MOF datasets, outperforming all previous results. This alignment of topological and chemical features enhances the predictive power and interpretability of CSTL, advancing understanding of structure-property relationships of MOFs and promoting efficient material discovery.

Figures

Figures reproduced from arXiv: 2412.11386 by the authors.

Figure 1
Figure 1. Schematic for Category-Specific Topological Models in MOF Property Prediction. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Comparison between predicted and true values for eight datasets on O [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. t-SNE feature reduction for category-specific topological features of MOF materials, where each green point [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Feature importance analysis for predictive models of eight properties in MOF materials using gradient [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Illustration of concepts in persistent homology. (a) An example of a simplicial complex. (b)-(e) Expansion [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]
Figure 6
Figure 6. Figure 6: Comparison between predicted and true values for eight datasets on O [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]
Figure 7
Figure 7. Figure 7: Heatmap of MAE values for predictive models across eight datasets related to O [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]
Figure 8
Figure 8. Figure 8: Heatmap of r2 values for predictive models across eight datasets related to O2/N2 selectivity properties in MOF materials. Panels (a)-(h) represent the MAE results for properties including Henry’s constant for N2 (a) and O2 (e), N2/O2 uptake (mol/kg) for N2 (b) and O2 …
Figure 9
Figure 9. Figure 9: Heatmap of RMSE values for predictive models across eight datasets related to O [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]
Figure 10
Figure 10. Figure 10: t-SNE feature reduction for category-specific topological features of MOF materials, where each green [PITH_FULL_IMAGE:figures/full_fig_p024_10.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

59 extracted references · 51 canonical work pages

  1. [1]

    The current status of mof and cof applications

    Ralph Freund, Orysia Zaremba, Giel Arnauts, Rob Ameloot, Grigorii Skorupskii, Mircea Dinc˘ a, Anastasiya Bavykina, Jorge Gascon, Aleksander Ejsmont, Joanna Goscianska, et al. The current status of mof and cof applications. Angewandte Chemie International Edition , 60 (45):23975–24001, 2021

    work page 2021

  2. [2]

    Green synthesis of metal–organic frameworks: A state-of-the-art review of potential environmental and medical applications

    Sandeep Kumar, Shikha Jain, Monika Nehra, Neeraj Dilbaghi, Giovanna Marrazza, and Ki- Hyun Kim. Green synthesis of metal–organic frameworks: A state-of-the-art review of potential environmental and medical applications. Coordination Chemistry Reviews, 420:213407, 2020

    work page 2020

  3. [3]

    Guest inclusion and structural dynamics in 2-d hydrogen-bonded metal- organic frameworks

    Chun-Long Chen and Alicia M Beatty. Guest inclusion and structural dynamics in 2-d hydrogen-bonded metal- organic frameworks. Journal of the American Chemical Society , 130 (51):17222–17223, 2008

    work page 2008

  4. [4]

    Chun-Long Chen, Andrea M Goforth, Mark D Smith, Cheng-Yong Su, and Hans-Conrad zur Loye. [co2 (ppca) 2 (h2o)(v4o12) 0.5]: a framework material exhibiting reversible shrinkage and expansion through a single-crystal-to-single-crystal transformation involving a change in the cobalt coordination environment. Angewandte Chemie International Edition , 44(41):66...

    work page 2005

  5. [5]

    Gas storage in porous metal–organic frameworks for clean energy applications

    Shengqian Ma and Hong-Cai Zhou. Gas storage in porous metal–organic frameworks for clean energy applications. Chemical Communications, 46(1):44–53, 2010

    work page 2010

  6. [6]

    Mof-based membranes for gas separations

    Qihui Qian, Patrick A Asinger, Moon Joo Lee, Gang Han, Katherine Mizrahi Rodriguez, Sharon Lin, Francesco M Benedetti, Albert X Wu, Won Seok Chi, and Zachary P Smith. Mof-based membranes for gas separations. Chemical reviews, 120(16):8161–8266, 2020

    work page 2020

  7. [7]

    Metal–organic framework materials as catalysts

    JeongYong Lee, Omar K Farha, John Roberts, Karl A Scheidt, SonBinh T Nguyen, and Joseph T Hupp. Metal–organic framework materials as catalysts. Chemical Society Reviews , 38(5):1450–1459, 2009

    work page 2009

  8. [8]

    Zirconium-based metal–organic frameworks for the catalytic hydrolysis of organophosphorus nerve agents

    Kent O Kirlikovali, Zhijie Chen, Timur Islamoglu, Joseph T Hupp, and Omar K Farha. Zirconium-based metal–organic frameworks for the catalytic hydrolysis of organophosphorus nerve agents. ACS applied materials & interfaces , 12(13):14702–14720, 2020

    work page 2020

Show all 59 references
  1. [9]

    Metal–organic framework materials as chemical sensors

    Lauren E Kreno, Kirsty Leong, Omar K Farha, Mark Allendorf, Richard P Van Duyne, and Joseph T Hupp. Metal–organic framework materials as chemical sensors. Chemical reviews, 112(2):1105–1125, 2012

    2012

  2. [10]

    High-throughput computational screening of metal– organic frameworks

    Yamil J Col´ on and Randall Q Snurr. High-throughput computational screening of metal– organic frameworks. Chemical Society Reviews, 43(16):5735–5749, 2014

    2014

  3. [11]

    Computational screening of trillions of metal–organic frameworks for high- performance methane storage

    Sangwon Lee, Baekjun Kim, Hyun Cho, Hooseung Lee, Sarah Yunmi Lee, Eun Seon Cho, and Jihan Kim. Computational screening of trillions of metal–organic frameworks for high- performance methane storage. ACS Applied Materials & Interfaces, 13(20):23647–23654, 2021

    2021

  4. [12]

    Density functional theory of electronic structure

    Walter Kohn, Axel D Becke, and Robert G Parr. Density functional theory of electronic structure. The journal of physical chemistry , 100(31):12974–12980, 1996. 25

    1996

  5. [13]

    Molecular dynamics simulations in biology

    Martin Karplus and Gregory A Petsko. Molecular dynamics simulations in biology. Nature, 347(6294):631–639, 1990

    1990

  6. [14]

    Hierarchical materials from high information content macromolecular building blocks: construction, dynamic inter- ventions, and prediction

    Li Shao, Jinrong Ma, Jesse L Prelesnik, Yicheng Zhou, Mary Nguyen, Mingfei Zhao, Samson A Jenekhe, Sergei V Kalinin, Andrew L Ferguson, Jim Pfaendtner, et al. Hierarchical materials from high information content macromolecular building blocks: construction, dynamic inter- vent...

    2022

  7. [15]

    Recent advances, opportunities, and challenges in high- throughput computational screening of mofs for gas separations

    Hilal Daglar and Seda Keskin. Recent advances, opportunities, and challenges in high- throughput computational screening of mofs for gas separations. Coordination Chemistry Reviews, 422:213470, 2020

    2020

  8. [16]

    Rational design of novel biomimetic sequence-defined polymers for mineralization applications

    Kaylyn Torkelson, Nada Y Naser, Xin Qi, Zhiliang Li, Wenchao Yang, Karthik Pushpavanam, Chun-Long Chen, Fran¸ cois Baneyx, and Jim Pfaendtner. Rational design of novel biomimetic sequence-defined polymers for mineralization applications. Chemistry of Materials , 36(2):786– 794, 2024

    2024

  9. [17]

    Computational and experimental determination of the properties, structure, and stability of peptoid nanosheets and nanotubes

    Mingfei Zhao, Shuai Zhang, Renyu Zheng, Sarah Alamdari, Christopher J Mundy, Jim Pfaendtner, Lilo D Pozzo, Chun-Long Chen, James J De Yoreo, and Andrew L Ferguson. Computational and experimental determination of the properties, structure, and stability of peptoid nanosheets an...

    2023

  10. [18]

    Influence of peptoid sequence on the mechanisms and kinetics of 2d assembly

    Sakshi Yadav Schmid, Xiang Ma, Joshua A Hammons, Sebastian T Mergelsberg, Bradley S Harris, Thomas Ferron, Wenchao Yang, Wenhao Zhou, Renyu Zheng, Shuai Zhang, et al. Influence of peptoid sequence on the mechanisms and kinetics of 2d assembly. ACS nano, 18 (4):3497–3508, 2024

    2024

  11. [19]

    Machine learning in materials science

    Jing Wei, Xuan Chu, Xiang-Yu Sun, Kun Xu, Hui-Xiong Deng, Jigen Chen, Zhongming Wei, and Ming Lei. Machine learning in materials science. InfoMat, 1(3):338–358, 2019

    2019

  12. [20]

    Mof synthesis prediction enabled by automatic data mining and machine learning

    Yi Luo, Saientan Bag, Orysia Zaremba, Adrian Cierpka, Jacopo Andreo, Stefan Wuttke, Pascal Friederich, and Manuel Tsotsalas. Mof synthesis prediction enabled by automatic data mining and machine learning. Angewandte Chemie International Edition , 61(19):e202200242, 2022

    2022

  13. [21]

    Applications of machine learning in metal-organic frameworks

    Sanggyu Chong, Sangwon Lee, Baekjun Kim, and Jihan Kim. Applications of machine learning in metal-organic frameworks. Coordination Chemistry Reviews, 423:213487, 2020

    2020

  14. [22]

    Development of the design and synthesis of metal–organic frameworks (mofs)–from large scale attempts, functional oriented modifications, to artificial intelligence (ai) predictions

    Zongsu Han, Yihao Yang, Joshua Rushlow, Jiatong Huo, Zhaoyi Liu, Yu-Chuan Hsu, Rujie Yin, Mengmeng Wang, Rongran Liang, Kun-Yu Wang, et al. Development of the design and synthesis of metal–organic frameworks (mofs)–from large scale attempts, functional oriented modifications, ...

    2025

  15. [23]

    Metal–organic framework with optimally selective xenon adsorption and separation

    Debasis Banerjee, Cory M Simon, Anna M Plonka, Radha K Motkuri, Jian Liu, Xianyin Chen, Berend Smit, John B Parise, Maciej Haranczyk, and Praveen K Thallapally. Metal–organic framework with optimally selective xenon adsorption and separation. Nature Communications, 7(1):1–7, 2016. 26

    2016

  16. [24]

    Progress toward the compu- tational discovery of new metal–organic framework adsorbents for energy applications

    Peyman Z Moghadam, Yongchul G Chung, and Randall Q Snurr. Progress toward the compu- tational discovery of new metal–organic framework adsorbents for energy applications. Nature Energy, 9(2):121–133, 2024

    2024

  17. [25]

    Advances, updates, and analytics for the computation-ready, experimental metal– organic framework database: Core mof 2019

    Yongchul G Chung, Emmanuel Haldoupis, Benjamin J Bucior, Maciej Haranczyk, Seulchan Lee, Hongda Zhang, Konstantinos D Vogiatzis, Marija Milisavljevic, Sanliang Ling, Jeffrey S Camp, et al. Advances, updates, and analytics for the computation-ready, experimental metal– organic ...

    2019

  18. [26]

    Large-scale screening of hypothetical metal–organic frameworks

    Christopher E Wilmer, Michael Leaf, Chang Yeon Lee, Omar K Farha, Brad G Hauser, Joseph T Hupp, and Randall Q Snurr. Large-scale screening of hypothetical metal–organic frameworks. Nature chemistry, 4(2):83–89, 2012

    2012

  19. [27]

    Prediction of o2/n2 selectivity in metal–organic frameworks via high-throughput computational screening and machine learning

    Ibrahim B Orhan, Hilal Daglar, Seda Keskin, Tu C Le, and Ravichandar Babarao. Prediction of o2/n2 selectivity in metal–organic frameworks via high-throughput computational screening and machine learning. ACS Applied Materials & Interfaces , 14(1):736–749, 2021

    2021

  20. [28]

    Using machine learning and data mining to leverage community knowledge for the engineering of stable metal–organic frameworks

    Aditya Nandy, Chenru Duan, and Heather J Kulik. Using machine learning and data mining to leverage community knowledge for the engineering of stable metal–organic frameworks. Journal of the American Chemical Society , 143(42):17535–17547, 2021

    2021

  21. [29]

    Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks

    Benjamin J Bucior, N Scott Bobbitt, Timur Islamoglu, Subhadip Goswami, Arun Gopalan, Taner Yildirim, Omar K Farha, Neda Bagheri, and Randall Q Snurr. Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks. Molecular Systems Design & Engineerin...

    2019

  22. [30]

    Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

    Andrew S Rosen, Shaelyn M Iyer, Debmalya Ray, Zhenpeng Yao, Alan Aspuru-Guzik, Laura Gagliardi, Justin M Notestein, and Randall Q Snurr. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery. Matter, 4(5):1578– 1597, 2021

    2021

  23. [31]

    Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties

    Tian Xie and Jeffrey C Grossman. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical review letters , 120(14):145301, 2018

    2018

  24. [32]

    A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks

    Yeonghun Kang, Hyunsoo Park, Berend Smit, and Jihan Kim. A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks. Nature Machine Intelligence, 5(3):309–318, 2023

    2023

  25. [33]

    Enhancing structure–property relationships in porous materials through transfer learning and cross-material few-shot learning

    Hyunsoo Park, Yeonghun Kang, and Jihan Kim. Enhancing structure–property relationships in porous materials through transfer learning and cross-material few-shot learning. ACS Applied Materials & Interfaces , 15(48):56375–56385, 2023

    2023

  26. [34]

    Moformer: self- supervised transformer model for metal–organic framework property prediction

    Zhonglin Cao, Rishikesh Magar, Yuyang Wang, and Amir Barati Farimani. Moformer: self- supervised transformer model for metal–organic framework property prediction. Journal of the American Chemical Society , 145(5):2958–2967, 2023. 27

    2023

  27. [35]

    Interpretable graph transformer network for predicting adsorption isotherms of metal–organic frameworks

    Pin Chen, Rui Jiao, Jinyu Liu, Yang Liu, and Yutong Lu. Interpretable graph transformer network for predicting adsorption isotherms of metal–organic frameworks. Journal of Chemical Information and Modeling , 62(22):5446–5456, 2022

    2022

  28. [36]

    Agl-score: algebraic graph learning score for protein– ligand binding scoring, ranking, docking, and screening

    Duc Duy Nguyen and Guo-Wei Wei. Agl-score: algebraic graph learning score for protein– ligand binding scoring, ranking, docking, and screening. Journal of chemical information and modeling, 59(7):3291–3304, 2019

    2019

  29. [37]

    Algebraic graph-assisted bidirectional transformers for molecular property prediction

    Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, and Feng Pan. Algebraic graph-assisted bidirectional transformers for molecular property prediction. Nature communications, 12(1):3521, 2021

    2021

  30. [38]

    Computing persistent homology

    Afra Zomorodian and Gunnar Carlsson. Computing persistent homology. In Proceedings of the twentieth annual symposium on Computational geometry , pages 347–356, 2004

    2004

  31. [39]

    Zixuan Cang and Guo-Wei Wei. Integration of element specific persistent homology and ma- chine learning for protein-ligand binding affinity prediction.International journal for numerical methods in biomedical engineering , 34(2):e2914, 2018

    2018

  32. [40]

    Path topology in molecular and materials sciences

    Dong Chen, Jian Liu, Jie Wu, Guo-Wei Wei, Feng Pan, and Shing-Tung Yau. Path topology in molecular and materials sciences. The journal of physical chemistry letters , 14(4):954–964, 2023

    2023

  33. [41]

    Persistent spectral graph

    Rui Wang, Duc Duy Nguyen, and Guo-Wei Wei. Persistent spectral graph. International journal for numerical methods in biomedical engineering , 36(9):e3376, 2020

    2020

  34. [42]

    Topologynet: Topology based deep convolutional and multi- task neural networks for biomolecular property predictions

    Zixuan Cang and Guo-Wei Wei. Topologynet: Topology based deep convolutional and multi- task neural networks for biomolecular property predictions. PLoS computational biology , 13 (7):e1005690, 2017

    2017

  35. [43]

    Topological represen- tations of crystalline compounds for the machine-learning prediction of materials properties

    Yi Jiang, Dong Chen, Xin Chen, Tangyi Li, Guo-Wei Wei, and Feng Pan. Topological represen- tations of crystalline compounds for the machine-learning prediction of materials properties. npj computational materials , 7(1):28, 2021

    2021

  36. [44]

    Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions

    Dong Chen, Jian Liu, and Guo-Wei Wei. Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions. Nature Machine Intelligence , 6(7):799– 810, 2024

    2024

  37. [45]

    Mathematical deep learning for pose and binding affinity prediction and ranking in d3r grand challenges

    Duc Duy Nguyen, Zixuan Cang, Kedi Wu, Menglun Wang, Yin Cao, and Guo-Wei Wei. Mathematical deep learning for pose and binding affinity prediction and ranking in d3r grand challenges. Journal of computer-aided molecular design , 33:71–82, 2019

    2019

  38. [46]

    Mathdl: mathematical deep learning for d3r grand challenge 4

    Duc Duy Nguyen, Kaifu Gao, Menglun Wang, and Guo-Wei Wei. Mathdl: mathematical deep learning for d3r grand challenge 4. Journal of computer-aided molecular design , 34:131–147, 2020

    2020

  39. [47]

    High- temperature carbon dioxide capture in a porous material with terminal zinc hydride sites

    Rachel C Rohde, Kurtis M Carsch, Matthew N Dods, Henry ZH Jiang, Alexandra R McIsaac, Ryan A Klein, Hyunchul Kwon, Sarah L Karstens, Yang Wang, Adrian J Huang, et al. High- temperature carbon dioxide capture in a porous material with terminal zinc hydride sites. Science, 386(6...

    2024

  40. [48]

    Geometry and topology for mesh generation

    Herbert Edelsbrunner. Geometry and topology for mesh generation . Cambridge University Press, 2001

    2001

  41. [49]

    Topology for computing, volume 16

    Afra J Zomorodian. Topology for computing, volume 16. Cambridge university press, 2005

    2005

  42. [50]

    On the Vietoris-Rips complexes and a cohomology theory for metric spaces

    Jean-Claude Hausmann et al. On the Vietoris-Rips complexes and a cohomology theory for metric spaces. Universit´ e de Gen` eve-Section de math´ ematiques, 1994

    1994

  43. [51]

    Elementary applied topology, volume 1

    Robert W Ghrist. Elementary applied topology, volume 1. Createspace Seattle, 2014

    2014

  44. [52]

    Smooth surfaces for multi-scale shape representation

    Herbert Edelsbrunner. Smooth surfaces for multi-scale shape representation. In International conference on foundations of software technology and theoretical computer science , pages 391–

  45. [53]

    Barcodes: the persistent topology of data

    Robert Ghrist. Barcodes: the persistent topology of data. Bulletin of the American Mathe- matical Society, 45(1):61–75, 2008

    2008

  46. [54]

    Scikit- learn: Machine learning in python

    Fabian Pedregosa, Ga¨ el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit- learn: Machine learning in python. the Journal of machine Learning research , 12:2825–2830, 2011

    2011

  47. [55]

    Persistent topological laplacians–a survey

    Xiaoqi Wei and Guo-Wei Wei. Persistent topological laplacians–a survey. arXiv preprint arXiv:2312.07563, 2023

    2023 arXiv

  48. [56]

    Neighborhood path complex for the quantitative analysis of the structure and stability of carboranes

    Jian Liu, Dong Chen, Feng Pan, and Jie Wu. Neighborhood path complex for the quantitative analysis of the structure and stability of carboranes. Journal of Computational Biophysics and Chemistry, 22(04):503–511, 2023

    2023

  49. [57]

    Neighborhood hypergraph model for topological data analysis

    Jian Liu, Dong Chen, Jingyan Li, and Jie Wu. Neighborhood hypergraph model for topological data analysis. Computational and Mathematical Biophysics , 10(1):262–280, 2022

    2022

  50. [58]

    Combinatorial algebraic topology , volume 21

    Dimitry Kozlov. Combinatorial algebraic topology , volume 21. Springer Science & Business Media, 2007

    2007

  51. [59]

    Evolutionary khovanov homology

    Li Shen, Jian Liu, and Guo-Wei Wei. Evolutionary khovanov homology. AIMS Mathematics , 9(9):26139–26165, 2024. 29

    2024

Pith tools

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