Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks

2021 ◽  
Vol 61 (2) ◽  
pp. 689-698
Author(s):  
Yashaswi Pathak ◽  
Sarvesh Mehta ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Networks ◽  
Free Energy ◽  
Solvation Free Energy ◽  
Energy Prediction ◽  
Atomic Interactions ◽  
Graph Neural Networks

scholarly journals Solvation Free Energy Prediction from Pairwise Atomistic Interactions by Machine Learning

2021 ◽  
Author(s):  
Hyuntae Lim ◽  
YounJoon Jung
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Chemical Properties ◽  
Solvation Energy ◽  
Solvation Free Energy ◽  
Learning Technologies ◽  
Training Data ◽  
Inner Product ◽  
Structure Property ◽  
Energy Prediction

Abstract Recent advances in machine learning technologies and their applications have led to the development of diverse structure-property relationship models for crucial chemical properties. The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic features calculates their interactions. The results of 6,493 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the interaction map shows that our model has significant potential for producing group contributions on the solvation energy, which indicates that the model provides provides not only predictions of target properties but also more detailed physicochemical insights.

scholarly journals Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery

2021 ◽  
Author(s):  
Peiyuan Gao ◽  
Xiu Yang ◽  
Yuhang Tang ◽  
Muqing Zheng ◽  
Amity Andersen ◽  
...  
Keyword(s):  
Free Energy ◽  
Organic Molecules ◽  
Equilibrium Constants ◽  
Gaussian Process Regression ◽  
Solvation Free Energy ◽  
Emergent Properties ◽  
Redox Potentials ◽  
Energy Prediction ◽  
Aqueous Solvation ◽  
Graphical Gaussian

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox...

scholarly journals MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Hyuntae Lim ◽  
YounJoon Jung
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Solvation Energy ◽  
Solvation Free Energy ◽  
Learning Technologies ◽  
Training Data ◽  
Inner Product ◽  
Structure Property ◽  
Energy Prediction ◽  
Feature Vectors

AbstractRecent advances in machine learning technologies and their applications have led to the development of diverse structure–property relationship models for crucial chemical properties. The solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between the two atomistic feature vectors calculates their interactions. The results of 6239 experimental measurements achieve outstanding performance and transferability for enlarging training data owing to its solvent-non-specific nature. An analysis of the interaction map shows that our model has significant potential for producing group contributions on the solvation energy, which indicates that the model provides not only predictions of target properties but also more detailed physicochemical insights.

scholarly journals Breaking the polar-nonpolar division in solvation free energy prediction

2017 ◽  
Vol 39 (4) ◽  
pp. 217-233 ◽  
Author(s):  
Bao Wang ◽  
Chengzhang Wang ◽  
Kedi Wu ◽  
Guo-Wei Wei
Keyword(s):  
Free Energy ◽  
Solvation Free Energy ◽  
Energy Prediction

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

Conversational Emotion Recognition Using Self-Attention Mechanisms and Graph Neural Networks

2020 ◽  
Author(s):  
Zheng Lian ◽  
Jianhua Tao ◽  
Bin Liu ◽  
Jian Huang ◽  
Zhanlei Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Graph Neural Networks
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