Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation
=================================================================================
Cigdem Altintas, Omer Faruk Altundal, Seda Keskin, and Ramazan Yildirim
Journal of Chemical Information and Modeling 2021 61 (5), 2131-2146
DOI: 10.1021/acs.jcim.1c00191

Transfer Learning Study of Gas Adsorption in Metal–Organic Frameworks
=================================================================================
Ruimin Ma, Yamil J. Colón, and Tengfei Luo
ACS Applied Materials & Interfaces 2020 12 (30), 34041-34048
DOI: 10.1021/acsami.0c06858

A Comprehensive Survey on Graph Neural Networks
=================================================================================
Wu, Zonghan et al
IEEE Transactions on Neural Networks and Learning Systems
Volume 32, Pages 4-24, 2019

Neural Message Passing for Quantum Chemistry
=================================================================================
https://github.com/brain-research/mpnn

Exploring Bayesian Optimization
=================================================================================
https://distill.pub/2020/bayesian-optimization/

A Survey of Deep Active Learning
=================================================================================
https://deepai.org/publication/a-survey-of-deep-active-learning. 

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning
=================================================================================
Z. Liu, L. Lin, Q. Jia, Z. Cheng, Y. Jiang, Y. Guo, and J. Ma, 
“Transferable multilevel attention neural network for accurate prediction of quantum chemistry properties via multitask learning,”
ACS Publications, 2021.

Multi-Task Learning on Graphs with Node and
Graph Level Labels
=================================================================================
https://grlearning.github.io/papers/132.pdf

An Overview of Multi-Task Learning in Deep Neural Networks
=================================================================================
S. Ruder, 
“An overview of multi-task learning in Deep Neural Networks,”
arXiv.org, 15-Jun-2017.
https://arxiv.org/abs/1706.05098v1

A Comprehensive Survey on Transfer Learning
=================================================================================
F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He,
“A comprehensive survey on Transfer Learning,” Cornell University, 07-Nov-2019. 
https://arxiv.org/abs/1911.02685

SIMPLE SPECTRAL GRAPH CONVOLUTION
=================================================================================
H. Zhu and P. Koniusz, “Simple spectral graph convolution,” 
OpenReview, 27-Mar-2021.
https://openreview.net/forum?id=CYO5T-YjWZV

A Survey On Graph Kernels
=================================================================================
Kriege, N.M., Johansson, F.D. & Morris, C.
Appl Netw Sci 5, 6 (2020).
https://doi.org/10.1007/s41109-019-0195-3

Fast geometric deep learning with continuous b-spline kernels
=================================================================================
Fey, M, Lenssen JE, Weichert F, Müller H (2018)
SplineCNN: Fast geometric deep learning with continuous b-spline kernels
In: IEEE Conference on Computer Vision and Pattern Recognition, 869–877.
https://doi.org/10.1109/cvpr.2018.00097.

A Survey on Deep Learning: Algorithms, Techniques, and Applications
=================================================================================
Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and S. S. Iyengar. 
2018.
ACM Comput. Surv. 51, 5, Article 92 (January 2019), 36 pages.
DOI:https://doi.org/10.1145/3234150

Graph Neural Network and Some of GNN Applications
=================================================================================
https://neptune.ai/blog/graph-neural-network-and-some-of-gnn-applications

Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning
=================================================================================
https://arxiv.org/abs/1904.08035