In this work, we developed a new representation of the chemical information for the machine learning models, with benefits from both the real space (R-space) and energy space (K-space). Different from the previous symmetric matrix presentations, the charge transfer channel based on Pauling's electronegativity is derived from the dependence on real space distance and orbitals for the heteroatomic structures. This representation can work for the bulk materials as well as the low dimensional nanomaterials, and can map the R-space and K-space into the pixel space (P-space) by training and testing 130k structures. P-space can well reproduce the R-space quantities within error 0.53. This new asymmetric matrix representation doubles the information storage than the previous symmetric representations. This work provides a new dimension for the computational chemistry towards the machine learning architecture.
This paper proposes an architecture for learning representations on molecules. The paper contains a number of typos, and presents results and an entire paragraph that is a near duplicate copy from prior work (which is not cited). In particular Table 1 is a near copy of Table 2 appearing in [1]. 
I find it particularly suspicious that the authors have a near copy of this prior table, which reports the ratio of MAE to chemical accuracy, and not MAE directly. The authors have the exact same numbers as this prior Table 2 but claim they are reporting MAE directly. Despite this table being a direct copy, it conveniently omits the columns from [1] which outperform the results presented here.
Also the paragraph describing this table is nearly identical to the paragraph in [1], that is the authors write
"These baselines include 5 different hand engineered molecular representations, which then get fed through a standard, off-the-shelf classifier. These input representations include the Coulomb Matrix (CM, Neese (2003)), BoB, Bonds Angles, Machine Learning (BAML, Huang & von Lilienfeld (2016)), Extended Connectivity Fingerprints (ECPF4, Rogers & Hahn (2010)), and Projected Histograms (HDAD, Faber et al. (2017a)) representations. In addition to these hand engineered features we include the Molecular Graph Convolutions model (GC, Kearnes et al. (2016)), the original GG-NN model(Li et al., 2015) trained with distance bins and DTNN."
In [1] it was written
"These baselines include 5 different hand engineered molecular representations, which then get fed through a standard, off-the-shelf classifier. These input representations include the Coulomb Matrix (CM, Rupp et al. (2012)), Bag of Bonds (BoB, Hansen et al. (2015)), Bonds Angles, Machine Learning (BAML, Huang & von Lilienfeld (2016)), Extended Connectivity Fingerprints (ECPF4, Rogers & Hahn (2010)), and “Projected Histograms” (HDAD, Faber et al. (2017)) representations. In addition to these hand engineered features we include two existing baseline MPNNs, the Molecular Graph Convolutions model (GC) from Kearnes et al. (2016), and the original GG-NN model Li et al. (2016) trained with distance bins."
The footnote in the table is also a duplicate copy of the footnote in [1].
Theirs: "As reported in DTNN. The model was trained on a different train/test split with 100k training samples vs
about 110k used in our experiments."
[1]: "As reported in Schutt et al. ¨ (2017). The model was trained on a different train/test split with 100k training samples vs 110k used in our experiments."
The proposed method itself is a variant of the MPNN framework introduced in [1], yet underperforms the original results in [1], as well as improved MPNNs (or GNNs) shown in [2,3]. 
1. https://arxiv.org/pdf/1704.01212.pdf
2. http://papers.nips.cc/paper/6700-schnet-a-continuous-filter-convolutional-neural-network-for-modeling-quantum-interactions
3. https://arxiv.org/pdf/1806.03146.pdf