Molecular Machine Learning in Chemical Process Design | Paper Citation Record

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This paper cites Greenman, Yunsie Chung, Shih-Cheng Li, David E.

Molecular Machine Learning in Chemical Process Design Greenman, Yunsie Chung, Shih-Cheng Li, David E

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This paper cites Graph neural networks for materials science and chemistry.

Molecular Machine Learning in Chemical Process Design Graph neural networks for materials science and chemistry

Reference 2

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This paper cites Gibbs–helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution.

Molecular Machine Learning in Chemical Process Design Gibbs–helmholtz graph neural network: capturing the temperature dependency of activity coefficients at infinite dilution

Reference 3

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This paper cites Schweidtmann, Jan G.

Molecular Machine Learning in Chemical Process Design Schweidtmann, Jan G

Reference 4

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This paper cites Vermeire and William H.

Molecular Machine Learning in Chemical Process Design Vermeire and William H

Reference 5

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This paper cites A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing.

Molecular Machine Learning in Chemical Process Design A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing

Reference 6

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Molecular Machine Learning in Chemical Process Design Unresolved cited work

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This paper cites Jones, and John M.

Molecular Machine Learning in Chemical Process Design Jones, and John M

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This paper cites COSMO-RS: An alternative to simulation for calculating thermodynamic properties of liquid mixtures.

Molecular Machine Learning in Chemical Process Design COSMO-RS: An alternative to simulation for calculating thermodynamic properties of liquid mixtures

Reference 9

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This paper cites Thermodynamics-consistent graph neural networks.

Molecular Machine Learning in Chemical Process Design Thermodynamics-consistent graph neural networks

Reference 10

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This paper cites HANNA: hard- constraint neural network for consistent activity coefficient prediction.

Molecular Machine Learning in Chemical Process Design HANNA: hard- constraint neural network for consistent activity coefficient prediction

Reference 11

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This paper cites Generative models for molecular discovery: Recent advances and challenges.

Molecular Machine Learning in Chemical Process Design Generative models for molecular discovery: Recent advances and challenges

Reference 12

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This paper cites Elton, Zois Boukouvalas, Mark D.

Molecular Machine Learning in Chemical Process Design Elton, Zois Boukouvalas, Mark D

Reference 13

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This paper cites Machine learning-aided generative molecular design.

Molecular Machine Learning in Chemical Process Design Machine learning-aided generative molecular design

Reference 14

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This paper cites Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back.

Molecular Machine Learning in Chemical Process Design Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

Reference 15

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This paper cites Continuous-molecular targeting for integrated solvent and process design.

Molecular Machine Learning in Chemical Process Design Continuous-molecular targeting for integrated solvent and process design

Reference 16

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This paper cites Babi, and Rafiqul Gani.

Molecular Machine Learning in Chemical Process Design Babi, and Rafiqul Gani

Reference 17

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This paper cites A hierarchical method to integrated solvent and process design of physical CO2 absorption using the saft-γ m ie approach.

Molecular Machine Learning in Chemical Process Design A hierarchical method to integrated solvent and process design of physical CO2 absorption using the saft-γ m ie approach

Reference 18

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Molecular Machine Learning in Chemical Process Design Unresolved cited work

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This paper cites Perturbed-chain SAFT: An equation of state based on a perturbation theory for chain molecules.

Molecular Machine Learning in Chemical Process Design Perturbed-chain SAFT: An equation of state based on a perturbation theory for chain molecules

Reference 20

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This paper cites Rittig, Karim Ben Hicham, Artur M.

Molecular Machine Learning in Chemical Process Design Rittig, Karim Ben Hicham, Artur M

Reference 21

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This paper cites Graph neural networks for the prediction of infinite dilution activity coefficients.

Molecular Machine Learning in Chemical Process Design Graph neural networks for the prediction of infinite dilution activity coefficients

Reference 22

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This paper cites Pooling solvent mixtures for solvation free energy predictions.

Molecular Machine Learning in Chemical Process Design Pooling solvent mixtures for solvation free energy predictions

Reference 23

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This paper cites Self-referencing embedded strings (selfies): A 100% robust molecular string representation.

Molecular Machine Learning in Chemical Process Design Self-referencing embedded strings (selfies): A 100% robust molecular string representation

Reference 24

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This paper cites SMILES, a chemical language and information system.

Molecular Machine Learning in Chemical Process Design SMILES, a chemical language and information system

Reference 25

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This paper cites Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, and Artur M.

Molecular Machine Learning in Chemical Process Design Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, and Artur M

Reference 26

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This paper cites word2vec, node2vec, graph2vec, x2vec: Towards a theory of vector embeddings of structured data.

Molecular Machine Learning in Chemical Process Design word2vec, node2vec, graph2vec, x2vec: Towards a theory of vector embeddings of structured data

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Molecular Machine Learning in Chemical Process Design Unresolved cited work

Reference 28

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This paper cites Coley, Regina Barzilay, William H.

Molecular Machine Learning in Chemical Process Design Coley, Regina Barzilay, William H

Reference 29

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This paper cites Extended-connectivity fingerprints.

Molecular Machine Learning in Chemical Process Design Extended-connectivity fingerprints

Reference 30

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This paper cites A review of molecular representation in the age of machine learning.

Molecular Machine Learning in Chemical Process Design A review of molecular representation in the age of machine learning

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This paper cites Geometric deep learning on molecular representations.

Molecular Machine Learning in Chemical Process Design Geometric deep learning on molecular representations

Reference 32

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This paper cites A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems.

Molecular Machine Learning in Chemical Process Design A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems

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This paper cites General purpose models for the chemical sciences.

Molecular Machine Learning in Chemical Process Design General purpose models for the chemical sciences

Reference 34

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This paper cites A graph representation of molecular ensembles for polymer property prediction.

Molecular Machine Learning in Chemical Process Design A graph representation of molecular ensembles for polymer property prediction

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This paper cites BigSMILES: a structurally-based line notation for describing macromolecules.

Molecular Machine Learning in Chemical Process Design BigSMILES: a structurally-based line notation for describing macromolecules

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This paper cites Quantum chemistry in the age of quantum computing.

Molecular Machine Learning in Chemical Process Design Quantum chemistry in the age of quantum computing

Reference 37

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This paper cites AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs.

Molecular Machine Learning in Chemical Process Design AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs

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This paper cites A practical guide to machine learning interatomic potentials–status and future.

Molecular Machine Learning in Chemical Process Design A practical guide to machine learning interatomic potentials–status and future

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This paper cites 3DReact: Geometric deep learning for chemical reactions.

Molecular Machine Learning in Chemical Process Design 3DReact: Geometric deep learning for chemical reactions

Reference 40

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This paper cites Attention is all you need.

Molecular Machine Learning in Chemical Process Design Attention is all you need

Reference 41

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This paper cites Schoenholz, Patrick F.

Molecular Machine Learning in Chemical Process Design Schoenholz, Patrick F

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This paper cites Transformers are Graph Neural Networks.

Molecular Machine Learning in Chemical Process Design Transformers are Graph Neural Networks

Reference 43

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This paper cites Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark.

Molecular Machine Learning in Chemical Process Design Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark

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Molecular Machine Learning in Chemical Process Design Self- supervised graph transformer on large-scale molecular data

Reference 45

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This paper cites On the scalability of gnns for molecular graphs.

Molecular Machine Learning in Chemical Process Design On the scalability of gnns for molecular graphs

Reference 46

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This paper cites Molecular graph transformer: stepping beyond alignn into long-range interactions.

Molecular Machine Learning in Chemical Process Design Molecular graph transformer: stepping beyond alignn into long-range interactions

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This paper cites Machine learning methods for small data challenges in molecular science.

Molecular Machine Learning in Chemical Process Design Machine learning methods for small data challenges in molecular science

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This paper cites Machine learning in polymer research.

Molecular Machine Learning in Chemical Process Design Machine learning in polymer research

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This paper cites Computer-aided molecular design of ionic liquids as advanced process media: a review from fundamentals to applications.

Molecular Machine Learning in Chemical Process Design Computer-aided molecular design of ionic liquids as advanced process media: a review from fundamentals to applications

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This paper cites GRAPPA–A hybrid graph neural network for predicting pure component vapor pressures.

Molecular Machine Learning in Chemical Process Design GRAPPA–A hybrid graph neural network for predicting pure component vapor pressures

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This paper cites A machine learning workflow for molecular analysis: application to melting points.

Molecular Machine Learning in Chemical Process Design A machine learning workflow for molecular analysis: application to melting points

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Molecular Machine Learning in Chemical Process Design Lansford, Klavs F

Reference 53

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This paper cites PUFFIN: A path-unifying feed-forward interfaced network for vapor pressure prediction.

Molecular Machine Learning in Chemical Process Design PUFFIN: A path-unifying feed-forward interfaced network for vapor pressure prediction

Reference 54

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This paper cites Understanding the language of molecules: predicting pure component parameters for the PC-SAFT equation of state from SMILES, 2025.

Molecular Machine Learning in Chemical Process Design Understanding the language of molecules: predicting pure component parameters for the PC-SAFT equation of state from SMILES, 2025

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This paper cites Predicting critical micelle concentrations for surfactants using graph convolutional neural networks.

Molecular Machine Learning in Chemical Process Design Predicting critical micelle concentrations for surfactants using graph convolutional neural networks

Reference 56

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This paper cites Graph neural networks for surfactant multi-property prediction.

Molecular Machine Learning in Chemical Process Design Graph neural networks for surfactant multi-property prediction

Reference 57

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This paper cites Machine learning for toxicity prediction using chemical structures: Pillars for success in the real world.

Molecular Machine Learning in Chemical Process Design Machine learning for toxicity prediction using chemical structures: Pillars for success in the real world

Reference 58

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This paper cites Human-and machine-centred designs of molecules and materials for sustainability and decarbonization.

Molecular Machine Learning in Chemical Process Design Human-and machine-centred designs of molecules and materials for sustainability and decarbonization

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This paper cites Machine learning-supported solvent design for lignin-first biore- fineries and lignin upgrading.

Molecular Machine Learning in Chemical Process Design Machine learning-supported solvent design for lignin-first biore- fineries and lignin upgrading

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This paper cites Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks.

Molecular Machine Learning in Chemical Process Design Predicting the temperature-dependent CMC of surfactant mixtures with graph neural networks

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This paper cites Van Lehn, and Victor M.

Molecular Machine Learning in Chemical Process Design Van Lehn, and Victor M

Reference 62

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This paper cites Rittig, Kobi C.

Molecular Machine Learning in Chemical Process Design Rittig, Kobi C

Reference 63

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This paper cites Machine learning of thermophysical properties.

Molecular Machine Learning in Chemical Process Design Machine learning of thermophysical properties

Reference 64

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This paper cites Neural recommender system for the activity coefficient prediction and UNIFAC model extension of ionic liquid–solute systems.AIChE Journal, 67(4):e17171, 2021.

Molecular Machine Learning in Chemical Process Design Neural recommender system for the activity coefficient prediction and UNIFAC model extension of ionic liquid–solute systems.AIChE Journal, 67(4):e17171, 2021

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This paper cites Spt-nrtl: A physics- guided machine learning model to predict thermodynamically consistent activity coefficients.

Molecular Machine Learning in Chemical Process Design Spt-nrtl: A physics- guided machine learning model to predict thermodynamically consistent activity coefficients

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Observation d78f4b3b-a420-43fc-8073-d0d8b260abb6 · outbound

This paper cites Graph machine learning for molecular property prediction and design.

Molecular Machine Learning in Chemical Process Design Graph machine learning for molecular property prediction and design

Reference 67

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Molecular Machine Learning in Chemical Process Design Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations

Reference 68

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Molecular Machine Learning in Chemical Process Design Schweidtmann, Jan G

Reference 69

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Molecular Machine Learning in Chemical Process Design Combining machine learning with physical knowledge in thermodynamic modeling of fluid mixtures

Reference 70

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This paper cites Ml-saft: a machine learning framework for pcp-saft parameter prediction.

Molecular Machine Learning in Chemical Process Design Ml-saft: a machine learning framework for pcp-saft parameter prediction

Reference 71

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Molecular Machine Learning in Chemical Process Design Predicting PC-SAFT pure-component pa- rameters by machine learning using a molecular fingerprint as key input

Reference 72

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Molecular Machine Learning in Chemical Process Design Germann, and Nicholas Lubbers

Reference 73

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Molecular Machine Learning in Chemical Process Design ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection

Reference 74

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Molecular Machine Learning in Chemical Process Design Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints

Reference 75

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Molecular Machine Learning in Chemical Process Design Development of a helmholtz free energy equation of state for fluid and solid phases via artificial neural networks

Reference 76

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Molecular Machine Learning in Chemical Process Design A review of large language models and autonomous agents in chemistry

Reference 77

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Molecular Machine Learning in Chemical Process Design ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models

Reference 78

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Molecular Machine Learning in Chemical Process Design Federated learning in chemical engineering: A tutorial on a framework for privacy-preserving collaboration across distributed data sources

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Molecular Machine Learning in Chemical Process Design Federated Learning from Molecules to Processes: A Perspective

Reference 80

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Molecular Machine Learning in Chemical Process Design Quantum chemistry structures and properties of 134 kilo molecules

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.827418Z

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This paper cites Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17.

Molecular Machine Learning in Chemical Process Design Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17

Reference 82

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This paper cites On the Opportunities and Risks of Foundation Models.

Molecular Machine Learning in Chemical Process Design On the Opportunities and Risks of Foundation Models

Reference 83

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Molecular Machine Learning in Chemical Process Design Analyzing learned molecular representations for property prediction

Reference 84

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Molecular Machine Learning in Chemical Process Design ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

Reference 85

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Molecular Machine Learning in Chemical Process Design Descriptor-based foundation models for molecular property prediction

Reference 86

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Molecular Machine Learning in Chemical Process Design Exploring data augmentation: Multi-task methods for molecular property prediction

Reference 87

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Molecular Machine Learning in Chemical Process Design Molecular property prediction in the ultra-low data regime

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.788790Z

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Molecular Machine Learning in Chemical Process Design Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Reference 89

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Molecular Machine Learning in Chemical Process Design $\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning

Reference 90

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Molecular Machine Learning in Chemical Process Design Explainable machine learning for property predictions in compound optimization

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.778969Z

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This paper cites Wellawatte, Heta A.

Molecular Machine Learning in Chemical Process Design Wellawatte, Heta A

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.768548Z

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This paper cites Explainability in graph neural networks: A taxonomic survey.

Molecular Machine Learning in Chemical Process Design Explainability in graph neural networks: A taxonomic survey

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.759173Z

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Molecular Machine Learning in Chemical Process Design Global concept explanations for graphs by contrastive learning

Reference 94

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This paper cites Hierarchical matrix completion for the prediction of properties of binary mixtures.

Molecular Machine Learning in Chemical Process Design Hierarchical matrix completion for the prediction of properties of binary mixtures

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.739445Z

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This paper cites From contrastive to abductive explanations and back again.

Molecular Machine Learning in Chemical Process Design From contrastive to abductive explanations and back again

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.730027Z

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Observation d0eb7bcf-5e01-4988-8014-c71f63f00ce3 · outbound

This paper cites Wellawatte, Aditi Seshadri, and Andrew D.

Molecular Machine Learning in Chemical Process Design Wellawatte, Aditi Seshadri, and Andrew D

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.720261Z

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Molecular Machine Learning in Chemical Process Design Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:46:03.710418Z

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This paper cites Uncertainty quantifica- tion for molecular property predictions with graph neural architecture search.

Molecular Machine Learning in Chemical Process Design Uncertainty quantifica- tion for molecular property predictions with graph neural architecture search

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:46:03.700857Z

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Observation ef50ea10-b7d9-48d0-9bda-c8724343bf22 · outbound

This paper cites Bayesian uncertainty quantification of graph neural networks using stochastic gradient Hamiltonian Monte Carlo.

Molecular Machine Learning in Chemical Process Design Bayesian uncertainty quantification of graph neural networks using stochastic gradient Hamiltonian Monte Carlo

Reference 100

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