Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-18T18:37:51.200663Z
Paper Citation Record · LEDGER
Thermodynamically consistent machine learning model for excess Gibbs energy
As of 28 July 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2509.06484.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-27T06:30:09.085275+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
55 of 55 outbound references displayed
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- verified fuzzy49
- unresolved0
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c8921765-b0ae-40c6-9935-d77289d59bd1 · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.808885Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation c710eff4-d1c3-4fbe-bcc2-cbc2fa222ada · outbound
This paper cites Abrams and John M.
Thermodynamically consistent machine learning model for excess Gibbs energy Abrams and John M
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.806115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 8eab3409-0931-4edf-b60c-9663e641a644 · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.803411Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 6c822946-5814-4aab-8d88-868eb6a95927 · outbound
This paper cites Marcilla, M.M.
Thermodynamically consistent machine learning model for excess Gibbs energy Marcilla, M.M
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.800541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 25e5e826-3d9d-4d3d-b9f6-6ff238cc7789 · outbound
This paper cites Jones, and John M.
Thermodynamically consistent machine learning model for excess Gibbs energy Jones, and John M
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.797957Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation dc877ff3-6405-4469-81e5-8c5df7272c63 · outbound
This paper cites Vapor-liquid equilibria by unifac group contribution.
Thermodynamically consistent machine learning model for excess Gibbs energy Vapor-liquid equilibria by unifac group contribution
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.795166Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 43ea0f0a-77db-434a-a943-1041885dc270 · outbound
This paper cites A modified unifac model.
Thermodynamically consistent machine learning model for excess Gibbs energy A modified unifac model
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.792484Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 92869801-9b6d-4774-81a3-e34e2dfff52e · outbound
This paper cites Further development of modified unifac (dortmund): Revision and extension 6.Journal of Chemical & Engineering Data, 61(8):2738–2748, May 2016.
Thermodynamically consistent machine learning model for excess Gibbs energy Further development of modified unifac (dortmund): Revision and extension 6.Journal of Chemical & Engineering Data, 61(8):2738–2748, May 2016
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.789530Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 94766eaf-a9e6-4761-b25b-7a2c05d2601c · outbound
This paper cites Unifac parameter table for prediction of liquid- liquid equilibriums.Industrial & Engineering Chemistry Process Design and Development, 20(2):331–339, April 1981.
Thermodynamically consistent machine learning model for excess Gibbs energy Unifac parameter table for prediction of liquid- liquid equilibriums.Industrial & Engineering Chemistry Process Design and Development, 20(2):331–339, April 1981
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.786168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 9526b6a3-cc2e-466c-a854-e5c9d5222518 · outbound
This paper cites Unifac model for ionic liquids.Industrial & Engineering Chemistry Research, 48(5):2697–2704, January 2009.
Thermodynamically consistent machine learning model for excess Gibbs energy Unifac model for ionic liquids.Industrial & Engineering Chemistry Research, 48(5):2697–2704, January 2009
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.783209Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 278d79d7-9aec-4aa9-8577-ec2d7df20506 · outbound
This paper cites Unifac model for ionic liquids: 3.
Thermodynamically consistent machine learning model for excess Gibbs energy Unifac model for ionic liquids: 3
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.780031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 72c47856-c5fa-4af9-a1e4-493c4eadb2e3 · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.776966Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 400b77e3-8e43-4c33-ac55-7170a37a904e · outbound
This paper cites Breitkreuz, Eckhard Ströfer, Jakob Burger, and Hans Hasse.
Thermodynamically consistent machine learning model for excess Gibbs energy Breitkreuz, Eckhard Ströfer, Jakob Burger, and Hans Hasse
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.773725Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 8a04d9c5-7e15-48ea-8f3e-126d3f9347fa · outbound
This paper cites Klamt and G.
Thermodynamically consistent machine learning model for excess Gibbs energy Klamt and G
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.881057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 875f4e72-510e-430c-92d3-abb29e026bc1 · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.878799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 4ee4ed55-577f-4a2f-8951-b22bcbd9af6d · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.883246Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 7fff924c-352c-4b68-8bf0-23a3120a9ea5 · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.876617Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 4ff57beb-7193-429f-b14d-baf376b049a8 · outbound
This paper cites An open source cosmo-rs implementation and parameterization supporting the efficient implementation of multiple segment descriptors.
Thermodynamically consistent machine learning model for excess Gibbs energy An open source cosmo-rs implementation and parameterization supporting the efficient implementation of multiple segment descriptors
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.873840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation a024fd86-6adf-4702-9f1e-651ea4320dfa · outbound
This paper cites Stubbs, J.
Thermodynamically consistent machine learning model for excess Gibbs energy Stubbs, J
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.871031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 3f9087e9-9a89-4587-8e81-aeff941611e5 · outbound
This paper cites Performance of cosmo-rs with sigma profiles from different model chemistries.Industrial & Engineering Chemistry Research, 46(20):6612–6629, September 2007.
Thermodynamically consistent machine learning model for excess Gibbs energy Performance of cosmo-rs with sigma profiles from different model chemistries.Industrial & Engineering Chemistry Research, 46(20):6612–6629, September 2007
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.885897Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation f8b9b523-4927-483c-be49-525df8e2d285 · outbound
This paper cites On the influence of basis sets and quantum chemical methods on the prediction accuracy of cosmo-rs.Physical Chemistry Chemical Physics, 13(48):21344.
Thermodynamically consistent machine learning model for excess Gibbs energy On the influence of basis sets and quantum chemical methods on the prediction accuracy of cosmo-rs.Physical Chemistry Chemical Physics, 13(48):21344
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.868332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 6b2b5785-73e0-45b6-b46c-df5ecf95a8cb · outbound
This paper cites Comprehensive assessment of cosmo-sac models for predictions of fluid-phase equilibria.Industrial & Engineering Chemistry Research, 56(35):9868–9884, August 2017.
Thermodynamically consistent machine learning model for excess Gibbs energy Comprehensive assessment of cosmo-sac models for predictions of fluid-phase equilibria.Industrial & Engineering Chemistry Research, 56(35):9868–9884, August 2017
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.865525Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation e2a776d5-d998-45e5-9dd3-54ad029f69d2 · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.862732Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation f949aa61-3371-49a6-a6b3-d251af9f0c07 · outbound
This paper cites Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions.
Thermodynamically consistent machine learning model for excess Gibbs energy Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.860329Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation b085c919-936b-41d4-b784-248212c71754 · outbound
This paper cites Spt-nrtl: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients.Fluid Phase Equilibria, 568:113731, May 2023.
Thermodynamically consistent machine learning model for excess Gibbs energy Spt-nrtl: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients.Fluid Phase Equilibria, 568:113731, May 2023
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.857361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation f6e22e77-75a2-4a5a-a16d-356c5e95dc63 · outbound
This paper cites Advancing thermodynamic group-contribution methods by machine learning: Unifac 2.0.Chemical Engineering Journal, 504:158667, January 2025.
Thermodynamically consistent machine learning model for excess Gibbs energy Advancing thermodynamic group-contribution methods by machine learning: Unifac 2.0.Chemical Engineering Journal, 504:158667, January 2025
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.854430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation f08d01f4-b0d1-4bae-852a-fd2c07e3b740 · outbound
This paper cites Modified unifac 2.0-a group-contribution method completed with machine learning.Industrial & Engineering Chemistry Research, 64(20):10304–10313, May 2025.
Thermodynamically consistent machine learning model for excess Gibbs energy Modified unifac 2.0-a group-contribution method completed with machine learning.Industrial & Engineering Chemistry Research, 64(20):10304–10313, May 2025
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.851704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 38fd69d0-6f2e-4210-a2b9-8913fb58032e · outbound
This paper cites Hanna: hard- constraint neural network for consistent activity coefficient prediction.Chemical Science, 15(47):19777–19786.
Thermodynamically consistent machine learning model for excess Gibbs energy Hanna: hard- constraint neural network for consistent activity coefficient prediction.Chemical Science, 15(47):19777–19786
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.764560Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 963a7bcc-f30c-47cc-8a7e-fa942aab0321 · outbound
This paper cites Smiles, a chemical language and information system.
Thermodynamically consistent machine learning model for excess Gibbs energy Smiles, a chemical language and information system
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.846547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation f7b8f989-c8cb-40eb-88de-ae98e5b12fe6 · outbound
This paper cites ChemBERTa-2: Towards Chemical Foundation Models.
Thermodynamically consistent machine learning model for excess Gibbs energy ChemBERTa-2: Towards Chemical Foundation Models
Reference 30
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T18:41:44.734459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 0c4f70ae-80be-47b7-bb57-a5ff4520b191 · outbound
This paper cites Enthalpies de formation des alliages liquides bismuth-étain-gallium à 723 k.
Thermodynamically consistent machine learning model for excess Gibbs energy Enthalpies de formation des alliages liquides bismuth-étain-gallium à 723 k
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.843920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 9aac638b-a7ec-49a0-92e2-2612185900d9 · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.747669Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation dd7d2f24-0d96-4575-ae95-809633870aa5 · outbound
This paper cites Grimm, and Jakob Burger.
Thermodynamically consistent machine learning model for excess Gibbs energy Grimm, and Jakob Burger
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.841296Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 47479a32-3cb8-4125-a053-c58e9400276e · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.838748Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 5763a43a-632a-4fcb-b834-3e53c5d07911 · outbound
This paper cites www.ddbst.com.
Thermodynamically consistent machine learning model for excess Gibbs energy www.ddbst.com
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.754336Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 573ad259-861b-4af8-8d91-92483e4484cf · outbound
This paper cites John Wiley & Sons, 2 edition.
Thermodynamically consistent machine learning model for excess Gibbs energy John Wiley & Sons, 2 edition
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.750791Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 3514da50-4cbf-4bbc-8a81-40c369f467fe · outbound
This paper cites an unresolved cited work.
Thermodynamically consistent machine learning model for excess Gibbs energy Unresolved cited work
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.833007Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 14c4cd57-ee59-4eb8-b2c6-a1dc92e7244b · outbound
This paper cites A new perspective on geometric thermodynamic models.Journal of Phase Equilibria and Diffusion, 40(5):715–724, October 2019.
Thermodynamically consistent machine learning model for excess Gibbs energy A new perspective on geometric thermodynamic models.Journal of Phase Equilibria and Diffusion, 40(5):715–724, October 2019
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.829778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 1001fd2b-46df-4c68-b6c3-658d1bd65796 · outbound
This paper cites A unified extrapolation thermody- namic model for multicomponent solutions based on binary data.Thermochimica Acta, 740:179824, October 2024.
Thermodynamically consistent machine learning model for excess Gibbs energy A unified extrapolation thermody- namic model for multicomponent solutions based on binary data.Thermochimica Acta, 740:179824, October 2024
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.826565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 8301d6d5-4cd6-402c-81e8-71f75d91c536 · outbound
This paper cites https://huggingface.co/DeepChem/ChemBERTa-77M-MTR, Last accessed: 08.07.2025.
Thermodynamically consistent machine learning model for excess Gibbs energy https://huggingface.co/DeepChem/ChemBERTa-77M-MTR, Last accessed: 08.07.2025
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.757896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 057a60d9-2350-4738-bc0c-7b2625e98be9 · outbound
This paper cites Selformer: molecular representation learning via selfies language models.Machine Learning: Science and Technology, 4(2):025035, June 2023.
Thermodynamically consistent machine learning model for excess Gibbs energy Selformer: molecular representation learning via selfies language models.Machine Learning: Science and Technology, 4(2):025035, June 2023
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.823324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation ab35aed3-fcfb-4c46-974d-2711a5fc0844 · outbound
This paper cites Self-referencing embedded strings (selfies): A 100Machine Learning: Science and Technology, 1(4):045024, October 2020.
Thermodynamically consistent machine learning model for excess Gibbs energy Self-referencing embedded strings (selfies): A 100Machine Learning: Science and Technology, 1(4):045024, October 2020
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.761119Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 07f72954-e223-4cfc-b04a-a676fcf32300 · outbound
This paper cites Convolutional Networks on Graphs for Learning Molecular Fingerprints.
Thermodynamically consistent machine learning model for excess Gibbs energy Convolutional Networks on Graphs for Learning Molecular Fingerprints
Reference 43
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T18:41:44.746717Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation f61d0a15-bbbd-4379-a031-68f61295d04f · outbound
This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.
Thermodynamically consistent machine learning model for excess Gibbs energy PyTorch: An Imperative Style, High-Performance Deep Learning Library
Reference 44
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T18:41:44.740589Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 90dd0e0a-810c-40e3-ace7-3ab70e85a58a · outbound
This paper cites Version: 2023.03.1.
Thermodynamically consistent machine learning model for excess Gibbs energy Version: 2023.03.1
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.820214Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation c4797f11-dd2a-462a-bf3e-c9fdb4e62cb0 · outbound
This paper cites geometric.
Thermodynamically consistent machine learning model for excess Gibbs energy geometric
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.770839Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 14f3b0c7-96db-4c13-b555-8acee16f04d6 · outbound
This paper cites Interpolation and extrapolation with the calphad method.Journal of Materials Science & Technology, 35(9):2115–2120, September 2019.
Thermodynamically consistent machine learning model for excess Gibbs energy Interpolation and extrapolation with the calphad method.Journal of Materials Science & Technology, 35(9):2115–2120, September 2019
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.817431Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation af520149-4ce0-4329-80d4-635b27f251e5 · outbound
This paper cites geometric.
Thermodynamically consistent machine learning model for excess Gibbs energy geometric
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.767739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 453e6534-0c20-4f11-9b8f-160d653c15fe · outbound
This paper cites Some aspects of multicomponent excess free energy models with subregular binaries.Geochimica et Cosmochimica Acta, 58(18):3763–3767, September 1994.
Thermodynamically consistent machine learning model for excess Gibbs energy Some aspects of multicomponent excess free energy models with subregular binaries.Geochimica et Cosmochimica Acta, 58(18):3763–3767, September 1994
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.814392Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation b008dbb9-af6a-419e-afb3-ddb3612f0aec · outbound
This paper cites Howald and Bimalendu N.
Thermodynamically consistent machine learning model for excess Gibbs energy Howald and Bimalendu N
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.848921Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation a03f90c1-1d1f-47c1-8858-ff0fcd507787 · outbound
This paper cites Deep Sets.
Thermodynamically consistent machine learning model for excess Gibbs energy Deep Sets
Reference 51
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T18:41:44.752574Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 38213ade-d2cd-44e3-9eea-c0f771e08678 · outbound
This paper cites Deiters and Thomas Kraska.High-Pressure Fluid Phase Equilibria.
Thermodynamically consistent machine learning model for excess Gibbs energy Deiters and Thomas Kraska.High-Pressure Fluid Phase Equilibria
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.836066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation c1be1dc3-6679-4fa0-be87-5b46342933e7 · outbound
This paper cites Pedregosa, G.
Thermodynamically consistent machine learning model for excess Gibbs energy Pedregosa, G
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-18T18:41:45.811570Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation 4f1cfeb7-3a29-4c0b-879e-6d36db9aeb5c · outbound
This paper cites Learning Smooth Neural Functions via Lipschitz Regularization.
Thermodynamically consistent machine learning model for excess Gibbs energy Learning Smooth Neural Functions via Lipschitz Regularization
Reference 54
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T18:41:44.758260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Observation f5c33b29-c3bc-4618-8cd2-f3816f620ce5 · outbound
This paper cites Spectral Normalization for Generative Adversarial Networks.
Thermodynamically consistent machine learning model for excess Gibbs energy Spectral Normalization for Generative Adversarial Networks
Reference 55
- Resolution
- verified exact
- local_arxiv, observed 2026-05-18T18:41:44.728017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.
Pith citing papers
No inbound Pith citation observations are available.