Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks

Hormazabal, Rodrigo S; Kang, Jeong Won; Park, Kiho; Yang, Dae Ryook

doi:10.1021/acs.jcim.2c00846

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…Deng et al 15 use a Graph Neural Network (GNN) to predict physical properties like solubility. Several studies take advantage of Graph Convolutional Networks (GCN) to predict the critical temperature of organic and inorganic compounds, 16 Abraham solute parameters, 17 and statistical torsion profiles. 18 But despite the limited success of these works, poor extrapolation beyond the training data remains a significant disadvantage of ML and artificial intelligence (AI) techniques in the field.…”

Section: ■ Introductionmentioning

confidence: 99%

Physics-Informed Neural Networks with Group Contribution Methods

Babaei

Stone

Knotts

et al. 2023

J. Chem. Theory Comput.

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Thermophysical properties of organic compounds are used in countless scientific, engineering, and industrial settings in developing theories, designing new systems and devices, analyzing costs and risks, and improving existing infrastructure. Often, due to costs, safety, prior interest, or procedural difficulties, experimental values for desired properties are not available and must be predicted. The literature is filled with prediction techniques, but even the best traditional methods have significant errors compared to what is possible considering experimental uncertainty. Recently, machine learning and artificial intelligence techniques have been applied to the property prediction problem, but the examples to date do not extrapolate well outside the data set used for training the model. This work demonstrates a solution to this problem by combining chemistry and physics when training the model and builds upon prior traditional and machine learning methods. Two case studies are presented. The first is for parachor which is used for surface tension prediction. Surface tensions are needed to design distillation columns, adsorption processes, gas−liquid reactors, liquid−liquid extractors, improve oil reservoir recovery, and undertake environmental impact studies or remediation actions. A set of 277 compounds is divided into training, validation, and test sets, and a multilayered physics-informed neural network (PINN) is developed. The results demonstrate that better extrapolation by deep learning models can be developed by adding in physics-based constraints. Second, a set of 1600 compounds is utilized for training, validating, and testing a PINN to improve normal boiling point predictions based on group contribution methods and physics-based constraints. The results show that the PINN performs better than any other method with a normal boiling point mean absolute error of 6.95 °C on training and 11.2 °C on test data. Key observations are that (1) a balanced split by compound type is important to have representative compound families in each of the train, validation, and test sets and (2) constraining group contributions being positive improves predictions on the test set. While this work demonstrates improvements for only surface tension and normal boiling point, the results offer significant hope that PINNs can improve prediction of other relevant thermophysical properties over existing approaches.

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Section: ■ Introductionmentioning

confidence: 99%

Physics-Informed Neural Networks with Group Contribution Methods

Babaei

Stone

Knotts

et al. 2023

J. Chem. Theory Comput.

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Open-Source Machine Learning in Computational Chemistry

Hagg

2023

J. Chem. Inf. Model.

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The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.

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Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks

Cited by 2 publications

References 41 publications

Physics-Informed Neural Networks with Group Contribution Methods

Physics-Informed Neural Networks with Group Contribution Methods

Open-Source Machine Learning in Computational Chemistry

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