Pith. sign in

REVIEW 2 minor 69 references

A Systematic Evaluation of Molecular Mixture Behavior Prediction

T0 review · 0 major / 2 minor · reviewed 2026-06-29 · grok-4.3

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

Pith's one-line read Machine learning models for molecular mixtures can show good overall accuracy while failing to capture non-ideal interactions between components.

desk verdict The paper's core contribution is an error decomposition framework plus matched datasets that show absolute accuracy on mixtures often hides weak recovery of non-ideal interactions. read the letter →

arxiv 2605.29698 v1 pith:NDOTVGCH submitted 2026-05-28 cs.LG physics.chem-ph

Roel J. Leenhouts , Nathan K. Morgan , William Green , Jan G. Rittig , Florence H. Vermeire This is my paper · ORCID
classification cs.LGphysics.chem-ph
keywords molecularmixturespropertypredictionmachinelearningevaluationframeworknon-idealbehaviordatasplitsmixturedatasets
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 develops an evaluation method that breaks down prediction errors in mixture properties into contributions from individual pure compounds and from deviations caused by mixing. It demonstrates that high accuracy on absolute values does not guarantee correct modeling of the extra effects from intermolecular interactions. When models are tested on mixtures involving molecules absent from training data, their ability to predict these non-ideal effects declines markedly. The work emphasizes the need for evaluation methods that go beyond total error to assess true mixture behavior.

What carries the argument

A decomposition framework using ideal-mixture baselines and excess-property metrics to separate pure-compound errors from interaction errors, paired with leakage-aware data splits.

What would settle it

Compare model predictions of excess properties on mixtures of entirely new molecules against experimental data to see if the non-ideal component error is low.

Watch

Extended reading notes

Core claim

The authors claim that absolute accuracy metrics in mixture property prediction can obscure poor performance on non-ideal components, with substantial drops under molecule-based splits that prevent leakage, identifying transfer to unseen molecules as the main challenge.

Load-bearing premise

Ideal mixture baselines and excess property calculations isolate non-ideal errors without interference from dataset biases or model assumptions.

Editorial extensions

If this is right

  • Evaluations of mixture models must include checks on excess properties to verify recovery of non-ideal behavior.
  • Performance on strict molecule splits provides a better indicator of generalization than random splits.
  • Curated matched datasets of pure and mixture properties enable more reliable benchmarking.
  • Models should be assessed for their ability to predict deviations from ideal mixing separately from pure component properties.

Reading between the lines

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

  • Explicit modeling of interaction terms could improve recovery of non-ideal effects beyond current end-to-end approaches.
  • Similar decomposition techniques might help in evaluating predictions for other complex systems like solutions or alloys.
  • The findings imply that larger and more diverse mixture datasets will be necessary to improve transfer performance.
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, simulated authors' rebuttal, and a circularity audit.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes an evaluation framework for machine learning models predicting physicochemical properties of molecular mixtures. The framework decomposes prediction error into pure-compound and non-ideal interaction (excess) components via ideal-mixture baselines and excess-property metrics, combined with leakage-aware splits. The authors curate seven matched pure-compound and mixture datasets and evaluate multiple model families, reporting that high absolute accuracy often masks poor recovery of non-ideal behavior while performance drops substantially under strict molecule-based splits. The central conclusion is that transfer to unseen molecules remains a key challenge in molecular mixture machine learning.

Significance. If the decomposition is shown to be free of confounding, the work would be significant for establishing a reproducible benchmarking standard that moves the field beyond absolute-error metrics toward isolating intermolecular interaction effects. The curation of matched datasets and the explicit comparison of absolute vs. excess metrics provide concrete evidence that current models struggle with generalization, which could guide future method development in a practically relevant domain.

minor comments (2)
  1. The abstract states the decomposition but does not include the explicit equation; adding it (or a reference to the methods section) would improve immediate clarity for readers.
  2. Figure or table captions should explicitly state the number of molecules in each strict split to allow quick assessment of the generalization gap magnitude.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of our work and the recommendation for minor revision. The report does not enumerate any specific major comments requiring point-by-point rebuttal.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical evaluation with independent baselines and metrics

full rationale

The paper is an evaluation study that curates datasets, applies leakage-aware splits, computes ideal-mixture baselines from pure-component data, and uses excess-property metrics to isolate non-ideal contributions. All reported findings (absolute accuracy masking non-ideal recovery, performance drop under molecule splits) are direct empirical observations from running existing model families on these datasets. No derivation, prediction, or uniqueness claim reduces by construction to fitted parameters, self-citations, or ansatzes; the decomposition is a definitional accounting identity using standard thermodynamic excess functions, not a self-referential result. Self-citations, if present, are not load-bearing for the central claims.

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

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond standard ML evaluation practices; the framework itself introduces no new physical postulates.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Systematic Evaluation of Molecular Mixture Behavior Prediction." pith.science (2026). https://pith.science/paper/NDOTVGCH

@misc{pith2026260529698,
  author       = {Pith},
  title        = {Pith review of: A Systematic Evaluation of Molecular Mixture Behavior Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NDOTVGCH}},
  note         = {Machine review of arXiv:2605.29698}
}
read the original abstract

Machine learning for molecular property prediction has focused largely on pure compounds, even though many practical applications depend on mixtures with intermolecular interactions. Recent work has expanded the availability of mixture datasets, but evaluation still focuses mainly on absolute accuracy. However, absolute errors in mixtures conflate pure-component contributions with deviations from ideal mixing. We propose an evaluation framework that decomposes mixture-property error into pure-compound and interaction (non-ideal) components. The framework combines leakage-aware split protocols, ideal-mixture baselines, and excess-property metrics. To support reproducible benchmarking, we curate seven matched pure and mixture physicochemical property datasets. Across multiple mixture-property tasks and model families, we find that strong absolute accuracy can mask poor recovery of non-ideal mixture behavior, and that performance drops substantially under strict molecule splits. These results identify transfer to unseen molecules as a central challenge in molecular mixture machine learning and motivate evaluation beyond absolute accuracy alone.

Figures

Figures reproduced from arXiv: 2605.29698 by the authors.

Figure 1
Figure 1. Left: target and predicted mixture-property curves, with shaded regions indicating excess [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Overview of the four model comparison axes: component featurization (orange), interaction [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Differences in absolute, excess, and ideal RMSE from pure-to-mixture to molecule splits [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Absolute and excess MAE vs. the ideal-mixture reference baseline under the mixture split. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Absolute and excess RMSE learning curves under the mixture split for [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Absolute and excess RMSE for temperature-context variants. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Distribution of mixture-property values and molecular weights across evaluation tasks. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: Distribution of number of components per mixture data point across evaluation datasets (6+ [PITH_FULL_IMAGE:figures/full_fig_p016_8.png]
Figure 9
Figure 9. Figure 9: Distribution of excess-property values across mixture datasets, where excess values are [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Differences in absolute, excess, and ideal RMSE from pure-to-mixture to molecule splits [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]
Figure 11
Figure 11. Figure 11: Absolute MAE vs. the ideal-mixture reference baseline under the mixture split, for all [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]
Figure 12
Figure 12. Figure 12: Excess MAE vs. the ideal-mixture reference baseline under the mixture split, for all [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]
Figure 13
Figure 13. Figure 13: Dataset-wise absolute-RMSE learning curves under the mixture split for [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: Dataset-wise excess-RMSE learning curves under the mixture split for [PITH_FULL_IMAGE:figures/full_fig_p024_14.png]
Figure 15
Figure 15. Figure 15: Fold-00 train/test temperature distributions for the solubility and viscosity mixture [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

69 extracted references · 58 canonical work pages

  1. [1]

    Muhieddine, Shekhar K

    Mohamad H. Muhieddine, Shekhar K. Viswanath, Alan Armstrong, Amparo Galindo, and Claire S. Adjiman. Model-based solvent selection for the synthesis and crystallisation of pharmaceutical compounds. Chemical Engineering Science, 264:118125, 2022. DOI: 10.1016/j.ces.2022.118125

    work page doi:10.1016/j.ces.2022.118125 2022

  2. [2]

    Huber, Sara Iborra, and Avelino Corma

    George W. Huber, Sara Iborra, and Avelino Corma. Synthesis of Transportation Fuels from Biomass: Chem- istry, Catalysts, and Engineering.Chemical Reviews, 106(9):4044–4098, 2006. DOI: 10.1021/cr068360d

    work page doi:10.1021/cr068360d 2006

  3. [3]

    Patel, Absar Lakdawala, Sajan Chourasia, and Rajesh N

    Paresh D. Patel, Absar Lakdawala, Sajan Chourasia, and Rajesh N. Patel. Bio fuels for compression ignition engine: A review on engine performance, emission and life cycle analysis.Renewable and Sustainable Energy Reviews, 65:24–43, 2016. DOI: 10.1016/j.rser.2016.06.010

    work page doi:10.1016/j.rser.2016.06.010 2016

  4. [4]

    Roger A. Sheldon. The E factor 25 years on: the rise of green chemistry and sustainability.Green Chem., 19:18–43, 2017. DOI: 10.1039/C6GC02157C

    work page doi:10.1039/c6gc02157c 2017

  5. [5]

    David J. C. Constable, Conchita Jimenez-Gonzalez, and Richard K. Henderson. Perspective on Solvent Use in the Pharmaceutical Industry.Organic Process Research & Development, 11(1):133–137, 2007. DOI: 10.1021/op060170h

    work page doi:10.1021/op060170h 2007

  6. [6]

    Clarke, Wei-Chien Tu, Oliver Levers, Andreas Bröhl, and Jason P

    Coby J. Clarke, Wei-Chien Tu, Oliver Levers, Andreas Bröhl, and Jason P. Hallett. Green and Sustainable Solvents in Chemical Processes.Chemical Reviews, 118(2):747–800, 2018. DOI: 10.1021/acs.chemrev.7b00571

    work page doi:10.1021/acs.chemrev.7b00571 2018

  7. [7]

    Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Research Square, 2024

    Zeqing Bao, Gary Tom, Austin Cheng, Alán Aspuru-Guzik, and Christine Allen. Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.Research Square, 2024. DOI: 10.21203/rs.3.rs-4170106/v1

    work page doi:10.21203/rs.3.rs-4170106/v1 2024

  8. [8]

    Cho, and H.C

    Bhupendra Singh Chauhan, Ram Kripal Singh, H.M. Cho, and H.C. Lim. Practice of diesel fuel blends using alternative fuels: A review.Renewable and Sustainable Energy Reviews, 59:1358–1368, 2016. DOI: 10.1016/j.rser.2016.01.062

    work page doi:10.1016/j.rser.2016.01.062 2016

Show all 69 references
  1. [9]

    G. M. Wilson and C. H. Deal. Activity Coefficients and Molecular Structure. Activity Coefficients in Changing Environments-Solutions of Groups.Industrial & Engineering Chemistry Fundamentals, 1(1): 20–23, 1962. DOI: 10.1021/i160001a003

    1962 doi

  2. [10]

    Jones, and John M

    Aage Fredenslund, Russell L. Jones, and John M. Prausnitz. Group-contribution estimation of activity coef- ficients in nonideal liquid mixtures.AIChE Journal, 21(6):1086–1099, 1975. DOI: 10.1002/aic.690210607

    1975 doi

  3. [11]

    Conductor-like screening model for real solvents: A new approach to the quantitative calculation of solvation phenomena.The Journal of Physical Chemistry, 99(7):2224–2235, 1995

    Andreas Klamt. Conductor-like screening model for real solvents: A new approach to the quantitative calculation of solvation phenomena.The Journal of Physical Chemistry, 99(7):2224–2235, 1995. DOI: 10.1021/j100007a062. 10

    1995 doi

  4. [12]

    HANNA: hard-constraint neural network for consistent activity coefficient prediction.Chemical Science, 15(47): 19777–19786, 2024

    Thomas Specht, Mayank Nagda, Sophie Fellenz, Stephan Mandt, Hans Hasse, and Fabian Jirasek. HANNA: hard-constraint neural network for consistent activity coefficient prediction.Chemical Science, 15(47): 19777–19786, 2024. DOI: 10.1039/d4sc05115g

    2024 doi

  5. [13]

    Vermeire and William H

    Florence H. Vermeire and William H. Green. Transfer learning for solvation free energies: From quantum chemistry to experiments.Chemical Engineering Journal, 418:129307, 2021. DOI: 10.1016/j.cej.2021.129307

    2021 doi

  6. [14]

    Rittig and Alexander Mitsos

    Jan G. Rittig and Alexander Mitsos. Thermodynamics-consistent graph neural networks.Chemical Science, 15:18504–18512, 2024. DOI: 10.1039/D4SC04554H

    2024 doi

  7. [15]

    Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S

    Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande. MoleculeNet: a benchmark for molecular machine learning.Chemical Science, 9:513–530, 2018. DOI: 10.1039/C7SC02664A

    2018 doi

  8. [16]

    Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik

    Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf H Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development. 2021. URLhttps://arxiv.org/abs/2102.09548

    2021

  9. [17]

    Dral, Matthias Rupp, and O

    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):140022, 2014. DOI: 10.1038/sdata.2014.22

    2014 doi

  10. [18]

    Segler, and Alain C

    Nathan Brown, Marco Fiscato, Marwin H.S. Segler, and Alain C. Vaucher. GuacaMol: Benchmarking Models for de Novo Molecular Design.Journal of Chemical Information and Modeling, 59(3):1096–1108,

  11. [19]

    DOI: 10.1021/acs.jcim.8b00839

    doi

  12. [20]

    Open Graph Benchmark: Datasets for Machine Learning on Graphs

    Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open Graph Benchmark: Datasets for Machine Learning on Graphs. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors,Ad- vances in Neural Infor...

    2020

  13. [21]

    Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

    Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley. Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors,Advances in Neural Information Processing Systems, volume 35, pages ...

    2022

  14. [22]

    Leenhouts, Nathan Morgan, Emad Al Ibrahim, William H

    Roel J. Leenhouts, Nathan Morgan, Emad Al Ibrahim, William H. Green, and Florence H. Vermeire. Pooling solvent mixtures for solvation free energy predictions.Chemical Engineering Journal, 513: 162232, 2025. DOI: 10.1016/j.cej.2025.162232

    2025 doi

  15. [23]

    Chew, Mohammad Atif Faiz Afzal, Zachary Kaplan, Eric M

    Alex K. Chew, Mohammad Atif Faiz Afzal, Zachary Kaplan, Eric M. Collins, Suraj Gattani, Mayank Misra, Anand Chandrasekaran, Karl Leswing, and Mathew D. Halls. Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures.npj Computat...

    2025 doi

  16. [24]

    Leenhouts, Tara Larsson, Sebastian Verhelst, and Florence H

    Roel J. Leenhouts, Tara Larsson, Sebastian Verhelst, and Florence H. Vermeire. Property prediction of fuel mixtures using pooled graph neural networks.Fuel, 381:133218, 2025. DOI: 10.1016/j.fuel.2024.133218

    2025 doi

  17. [25]

    Machine learning for predicting the viscosity of binary liquid mixtures.Chemical Engineering Journal, 464:142454, 2023

    Camille Bilodeau, Andrei Kazakov, Sukrit Mukhopadhyay, Jillian Emerson, Tom Kalantar, Chris Muzny, and Klavs Jensen. Machine learning for predicting the viscosity of binary liquid mixtures.Chemical Engineering Journal, 464:142454, 2023. DOI: 10.1016/j.cej.2023.142454

    2023 doi

  18. [26]

    Mani Sarathy

    Nursulu Kuzhagaliyeva, Samuel Horváth, John Williams, Andre Nicolle, and S. Mani Sarathy. Artificial intelligence-driven design of fuel mixtures.Communications Chemistry, 5(1), 2022. DOI: 10.1038/s42004- 022-00722-3

    2022 doi

  19. [27]

    MixtureSolDB, dataset of solubility values for organic compounds in binary mixtures of solvents at various temperatures.ChemRxiv, 2025

    Dmitry Malikov, Lev Krasnov, Marina Kiseleva, Elizaveta Meshcheriakova, Fedor Kuznetsov, Vladimir Elistratov, Matvei Vasiyarov, Sergei Tatarin, and Stanislav Bezzubov. MixtureSolDB, dataset of solubility values for organic compounds in binary mixtures of solvents at various te...

    2025 doi

  20. [28]

    Mejía-Mendoza, Seyed Mohamad Moosavi, and Benjamin Manuel Sanchez

    Ella Miray Rajaonson, Mahyar Rajabi Kochi, Luis M. Mejía-Mendoza, Seyed Mohamad Moosavi, and Benjamin Manuel Sanchez. CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems Data...

    2025

  21. [29]

    J. M. Prausnitz, R. N. Lichtenthaler, and Edmundo Gomes de Azevedo.Molecular thermodynamics of fluid-phase equilibria third edition. Prentice Hall, 1999

    1999

  22. [30]

    Henri Renon and J. M. Prausnitz. Local compositions in thermodynamic excess functions for liquid mixtures.AIChE Journal, 14(1):135–144, 1968. DOI: 10.1002/aic.690140124

    1968 doi

  23. [31]

    Martins, Márcio J

    Rosana J. Martins, Márcio J. E. de M. Cardoso, and Oswaldo E. Barcia. Excess Gibbs Free Energy Model for Calculating the Viscosity of Binary Liquid Mixtures.Industrial & Engineering Chemistry Research, 39 (3):849–854, 2000. DOI: 10.1021/ie990398b

    doi

  24. [32]

    Machine learning for predicting thermodynamic proper- ties of pure fluids and their mixtures.Energy, 188:116091, 2019

    Yuanbin Liu, Weixiang Hong, and Bingyang Cao. Machine learning for predicting thermodynamic proper- ties of pure fluids and their mixtures.Energy, 188:116091, 2019. DOI: 10.1016/j.energy.2019.116091

    2019 doi

  25. [33]

    A systematic study of key elements underlying molecular property prediction.Nature Communications, 14(1), 2023

    Jianyuan Deng, Zhibo Yang, Hehe Wang, Iwao Ojima, Dimitris Samaras, and Fusheng Wang. A systematic study of key elements underlying molecular property prediction.Nature Communications, 14(1), 2023. DOI: 10.1038/s41467-023-41948-6

    2023 doi

  26. [34]

    Blumenthal, and Olga V

    Roman Joeres, David B. Blumenthal, and Olga V . Kalinina. Data splitting to avoid information leakage with DataSAIL.Nature Communications, 16(1), 2025. DOI: 10.1038/s41467-025-58606-8

    2025 doi

  27. [35]

    Schweidtmann, Jan G

    Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, and Alexander Mitsos. Physical pooling functions in graph neural networks for molecular property prediction. Computers & Chemical Engineering, 172:108202, 2023. DOI: 10.1016/j.compc...

    2023 doi

  28. [36]

    Dobbelaere, István Lengyel, Christian V

    Maarten R. Dobbelaere, István Lengyel, Christian V . Stevens, and Kevin M. Van Geem. Geometric deep learning for molecular property predictions with chemical accuracy across chemical space.Journal of Cheminformatics, 16(1), 2024. DOI: 10.1186/s13321-024-00895-0

    2024 doi

  29. [37]

    McGill, Florence H

    Esther Heid, Charles J. McGill, Florence H. Vermeire, and William H. Green. Characterizing Uncertainty in Machine Learning for Chemistry.Journal of Chemical Information and Modeling, 63(13):4012–4029,

  30. [38]

    DOI: 10.1021/acs.jcim.3c00373

    doi

  31. [39]

    Van Lehn, and Victor M

    Shiyi Qin, Shengli Jiang, Jianping Li, Prasanna Balaprakash, Reid C. Van Lehn, and Victor M. Zavala. Capturing molecular interactions in graph neural networks: a case study in multi-component phase equilibrium.Digital Discovery, 2:138–151, 2023. DOI: 10.1039/D2DD00045H

    2023 doi

  32. [40]

    Di Wu, Zutao Zhu, Jun Zhang, Huaqiang Wen, Saimeng Jin, and Weifeng Shen. An Interpretable Solute–Solvent Interactive Attention Module Intensified Graph-Learning Architecture toward Enhancing the Prediction Accuracy of an Infinite Dilution Activity Coefficient.Industrial & Eng...

    2024 doi

  33. [41]

    A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing.Digital Discovery, 1:859–869, 2022

    Benedikt Winter, Clemens Winter, Johannes Schilling, and André Bardow. A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing.Digital Discovery, 1:859–869, 2022. DOI: 10.1039/D2DD00058J

    2022 doi

  34. [42]

    Molecular machine learning in chemical process design.Current Opinion in Chemical Engineering, 52:101239, 2026

    Jan G Rittig, Manuel Dahmen, Martin Grohe, Philippe Schwaller, and Alexander Mitsos. Molecular machine learning in chemical process design.Current Opinion in Chemical Engineering, 52:101239, 2026

    2026

  35. [43]

    Rittig, Kobi C

    Jan G. Rittig, Kobi C. Felton, Alexei A. Lapkin, and Alexander Mitsos. Gibbs–Duhem-informed neu- ral networks for binary activity coefficient prediction.Digital Discovery, 2:1752–1767, 2023. DOI: 10.1039/D3DD00103B

    2023 doi

  36. [44]

    Rondinelli, and Wei Chen

    Hengrui Zhang, Tianxing Lai, Jie Chen, Arumugam Manthiram, James M. Rondinelli, and Wei Chen. Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network.PRX Energy, 3: 023006, 2024. DOI: 10.1103/PRXEnergy.3.023006

    2024 doi

  37. [45]

    Enis Leblebici

    Ulderico Di Caprio, Florence Vermeire, Tom Van Gerven, and M. Enis Leblebici. Physics-informed machine learning predicting CO2 capture performances of organic mixtures.Chemical Engineering and Processing - Process Intensification, 216:110410, 2025. DOI: 10.1016/j.cep.2025.110410

    2025 doi

  38. [46]

    Rittig, Elie Akanny, Sandip Bhattacharya, Christina Kohlmann, and Alexander Mitsos

    Christoforos Brozos, Jan G. Rittig, Elie Akanny, Sandip Bhattacharya, Christina Kohlmann, and Alexander Mitsos. Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks. Computers & Chemical Engineering, 198:109085, 2025. DOI: 10.1016/j.compch...

    2025 doi

  39. [47]

    Deep Neural Networks for Multicomponent Molecular Systems.ACS Omega, 5(33): 21042–21053, 2020

    Kyohei Hanaoka. Deep Neural Networks for Multicomponent Molecular Systems.ACS Omega, 5(33): 21042–21053, 2020. DOI: 10.1021/acsomega.0c02599

    2020 doi

  40. [48]

    BP-Kelley/descriptastorus: Descriptor computation(chemistry) and (optional) storage for Machine Learning, 2026

    Bp-Kelley. BP-Kelley/descriptastorus: Descriptor computation(chemistry) and (optional) storage for Machine Learning, 2026. URL https://github.com/bp-kelley/descriptastorus. Accessed: May 4, 2026. 12

    2026

  41. [49]

    Mordred: a molecular descriptor calculator.Journal of Cheminformatics, 10(1):4, 2018

    Hirotomo Moriwaki, Yu-Shi Tian, Norihito Kawashita, and Tatsuya Takagi. Mordred: a molecular descriptor calculator.Journal of Cheminformatics, 10(1):4, 2018. DOI: 10.1186/s13321-018-0258-y

    2018 doi

  42. [50]

    Extended-Connectivity Fingerprints.Journal of Chemical Information and Modeling, 50(5):742–754, 2010

    David Rogers and Mathew Hahn. Extended-Connectivity Fingerprints.Journal of Chemical Information and Modeling, 50(5):742–754, 2010. DOI: 10.1021/ci100050t

    2010 doi

  43. [51]

    Translation be- tween Molecules and Natural Language

    Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. Translation be- tween Molecules and Natural Language. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang, editors, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processi...

    2022 doi

  44. [52]

    Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction.Wireless Communications and Mobile Computing, 2021(1):7181815, 2021

    Juncai Li and Xiaofei Jiang. Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction.Wireless Communications and Mobile Computing, 2021(1):7181815, 2021. DOI: 10.1155/2021/7181815

    2021 doi

  45. [53]

    Analyzing Learned Molecular Representations for Property Prediction

    Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, V olker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay. Analyzing Learned Molecular Representations for Prop...

    2019 doi

  46. [54]

    Schoenholz, Patrick F

    Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. Neural Message Passing for Quantum Chemistry. In Doina Precup and Yee Whye Teh, editors,Proceedings of the 34th International Conference on Machine Learning, volume 70 ofProceedings of Ma...

    2017

  47. [55]

    Greenman, Yunsie Chung, Shih-Cheng Li, David E

    Esther Heid, Kevin P. Greenman, Yunsie Chung, Shih-Cheng Li, David E. Graff, Florence H. Vermeire, Haoyang Wu, William H. Green, and Charles J. McGill. Chemprop: A Machine Learning Package for Chemical Property Prediction.Journal of Chemical Information and Modeling, 64(1):9–1...

    2024 doi

  48. [56]

    Fabian Jirasek and Hans Hasse. Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.Annual Review of Chemical and Biomolecular Engineering, 14(V olume 14, 2023):31–51, 2023. DOI: 10.1146/annurev-chembioeng-092220-025342

    2023 doi

  49. [57]

    Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution.Digital Discovery, 2:781–798, 2023

    Edgar Ivan Sanchez Medina, Steffen Linke, Martin Stoll, and Kai Sundmacher. Gibbs–Helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution.Digital Discovery, 2:781–798, 2023. DOI: 10.1039/D2DD00142J

    2023 doi

  50. [58]

    BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various tempera- tures.Scientific Data, 12(1):1236, 2025

    Lev Krasnov, Dmitry Malikov, Marina Kiseleva, Sergei Tatarin, Sergey Sosnin, and Stanislav Bezzubov. BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various tempera- tures.Scientific Data, 12(1):1236, 2025. DOI: 10.1038/s41597-025-05559-8

    2025 doi

  51. [59]

    Machine learning for fuel property predictions: A multi-task and transfer learning approach.SAE Technical Paper Series, 1, 2023

    Tara Larsson, Florence Vermeire, and Sebastian Verhelst. Machine learning for fuel property predictions: A multi-task and transfer learning approach.SAE Technical Paper Series, 1, 2023. DOI: 10.4271/2023-01- 0337

    2023 doi

  52. [60]

    Graff, Nathan K

    David E. Graff, Nathan K. Morgan, Jackson W. Burns, Anna C. Doner, Brian Li, Shih-Cheng Li, Joel Manu, Angiras Menon, Hao-Wei Pang, Haoyang Wu, Akshat Shirish Zalte, Jonathan W. Zheng, Connor W. Coley, William H. Green, and Kevin P. Greenman. Chemprop v2: An Efficient, Modular...

  53. [61]

    DOI: 10.1021/acs.jcim.5c02332

    doi

  54. [62]

    Kovary, Desmond Gilmour, thibaultvarin r, Jackson Burns, Julien St-Laurent, t, DomInvivo, Saurav Maheshkar, and rbyrne momatx

    Emmanuel Noutahi, Cas Wognum, Hadrien Mary, Honoré Hounwanou, Kyle M. Kovary, Desmond Gilmour, thibaultvarin r, Jackson Burns, Julien St-Laurent, t, DomInvivo, Saurav Maheshkar, and rbyrne momatx. datamol-io/molfeat: 0.9.4, 2023. URLhttps://doi.org/10.5281/zenodo.8373019

    2023 doi

  55. [63]

    RDKit: Open-source cheminformatics., 2026

    Greg Landrum. RDKit: Open-source cheminformatics., 2026. URL https://www.rdkit.org/. Ac- cessed: May 4, 2026

    2026

  56. [64]

    Deep Sets

    Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, and Alexander Smola. Deep Sets. InAdvances in Neural Information Processing Sys- tems 30, 2017. URL https://proceedings.neurips.cc/paper_files/paper/2017/file/ f22e4747da1aa27e363d86d40ff4...

    2017

  57. [65]

    Attentive Pooling Networks.arXiv preprint arXiv:1602.03609, 2016

    Cicero dos Santos, Bing Xiang, and Bowen Zhou. Attentive Pooling Networks.arXiv preprint arXiv:1602.03609, 2016. URLhttps://arxiv.org/abs/1602.03609. 13

    2016 arXiv

  58. [66]

    Order Matters: Sequence to Sequence for Sets.arXiv preprint arXiv:1511.06391, 2016

    Oriol Vinyals, Samy Bengio, and Manjunath Kudlur. Order Matters: Sequence to Sequence for Sets.arXiv preprint arXiv:1511.06391, 2016. URLhttps://arxiv.org/abs/1511.06391

    2016 arXiv

  59. [67]

    XGBoost: A Scalable Tree Boosting System

    Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. InProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 785–794, New York, NY , USA, 2016. Association for Computing Machinery. DOI: 10.1145/2...

    2016 doi

  60. [68]

    Massively Parallel Hyperparameter Tuning.CoRR, abs/1810.05934, 2018

    Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar. Massively Parallel Hyperparameter Tuning.CoRR, abs/1810.05934, 2018. URL http://arxiv.org/abs/1810.05934

    2018

  61. [69]

    Mole fraction of cmpi

    Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica. Tune: A Research Platform for Distributed Model Selection and Training.arXiv preprint arXiv:1807.05118, 2018. A Technical Appendices and Supplementary Material This appendix collects...

    2018 arXiv

Pith tools

Reviewed June 29, 2026 · model on record in the stance chip above.