scholarly journals Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers

2021 ◽  
Vol 154 (2) ◽  
pp. 024906
Author(s):  
Chee-Kong Lee ◽  
Chengqiang Lu ◽  
Yue Yu ◽  
Qiming Sun ◽  
Chang-Yu Hsieh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Optoelectronic Properties ◽  
Conjugated Oligomers ◽  
Graph Neural Networks

scholarly journals Transfer Learning with Graph Neural Networks for Optoelectronic Properties of Conjugated Oligomers

2020 ◽  
Author(s):  
Chee Kong Lee ◽  
Chengqiang Lu ◽  
Yue Yu ◽  
Qiming Sun ◽  
Chang-Yu Hsieh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Excited State ◽  
Transfer Learning ◽  
State Energy ◽  
Optoelectronic Properties ◽  
Training Data ◽  
Conjugated Oligomers ◽  
Excited State Energy ◽  
Tddft Calculations ◽  
Graph Neural Networks

Despite the remarkable progress of machine learning (ML) techniques in chemistry, modeling the optoelectronic properties of long conjugated oligomers and polymers with ML remains challenging due to the difficulty in obtaining sufficient training data. Here we use transfer learning to address the data scarcity issue by pre-training graph neural networks using data from short oligomers. With only a few hundred training data, we are able to achieve an average error of about 0.1 eV for excited state energy of oligothiophenes against TDDFT calculations. We show that the success of our transfer learning approach relies on the relative locality of low-lying electronic excitations in long conjugated oligomers. Finally, we demonstrate the transferability of our approach by modeling the lowest-lying excited-state energies of poly(3-hexylthiopnene) (P3HT) in its single-crystal and solution phases using the transfer learning models trained with data of gas-phase oligothiophenes. The transfer learning predicted excited-state energy distributions agree quantitatively with TDDFT calculations and capture some important qualitative features observed in experimental absorption spectra.<br>

scholarly journals Transfer Learning with Graph Neural Networks for Optoelectronic Properties of Conjugated Oligomers

2020 ◽  
Author(s):  
Chee Kong Lee ◽  
Chengqiang Lu ◽  
Yue Yu ◽  
Qiming Sun ◽  
Chang-Yu Hsieh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Excited State ◽  
Transfer Learning ◽  
State Energy ◽  
Optoelectronic Properties ◽  
Training Data ◽  
Conjugated Oligomers ◽  
Excited State Energy ◽  
Tddft Calculations ◽  
Graph Neural Networks

Despite the remarkable progress of machine learning (ML) techniques in chemistry, modeling the optoelectronic properties of long conjugated oligomers and polymers with ML remains challenging due to the difficulty in obtaining sufficient training data. Here we use transfer learning to address the data scarcity issue by pre-training graph neural networks using data from short oligomers. With only a few hundred training data, we are able to achieve an average error of about 0.1 eV for excited state energy of oligothiophenes against TDDFT calculations. We show that the success of our transfer learning approach relies on the relative locality of low-lying electronic excitations in long conjugated oligomers. Finally, we demonstrate the transferability of our approach by modeling the lowest-lying excited-state energies of poly(3-hexylthiopnene) (P3HT) in its single-crystal and solution phases using the transfer learning models trained with data of gas-phase oligothiophenes. The transfer learning predicted excited-state energy distributions agree quantitatively with TDDFT calculations and capture some important qualitative features observed in experimental absorption spectra.<br>

scholarly journals Learning Multi-Task Communication with Message Passing for Sequence Learning

2019 ◽  
Vol 33 ◽  
pp. 4360-4367 ◽  
Author(s):  
Pengfei Liu ◽  
Jie Fu ◽  
Yue Dong ◽  
Xipeng Qiu ◽  
Jackie Chi Kit Cheung
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Text Classification ◽  
Sequence Learning ◽  
Message Passing ◽  
Ad Hoc ◽  
General Graph ◽  
Learning Framework ◽  
Task Learning ◽  
Graph Neural Networks

We present two architectures for multi-task learning with neural sequence models. Our approach allows the relationships between different tasks to be learned dynamically, rather than using an ad-hoc pre-defined structure as in previous work. We adopt the idea from message-passing graph neural networks, and propose a general graph multi-task learning framework in which different tasks can communicate with each other in an effective and interpretable way. We conduct extensive experiments in text classification and sequence labelling to evaluate our approach on multi-task learning and transfer learning. The empirical results show that our models not only outperform competitive baselines, but also learn interpretable and transferable patterns across tasks.

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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