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Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials.
The inorganic crystal structure data base
Bergerhoff, G., Hundt, R., Sievers, R., and Brown, I. D. (1983) · 1983
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A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F. (2005) · 2005
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VORO++: a three-dimensional voronoi cell library in c++
Rycroft, C. H. (2009) · 2009
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G. (2009) · 2009
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Extended-connectivity fingerprints
Rogers, D. and Hahn, M. (2010) · 2010
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Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C., and Reymond, J.-L. (2012) · 2012
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Fast and accurate modeling of molecular atomization energies with machine learning
Rupp, M., Tkatchenko, A., Müller, K.-R., and von Lilienfeld, O. A. (2012) · 2012
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2013) · 2013
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The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., and Persson, K. a. (2013) · 2013
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Materials design and discovery with High-Throughput density functional theory: The open quantum materials database (OQMD)
Saal, J. E., Kirklin, S., Aykol, M., Meredig, B., and Wolverton, C. (2013) · 2013
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J. (2014) · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and von Lilienfeld, O. A. (2014) · 2014
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P. (2015) · 2015
Cited alongside, same era.
Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R. (2015) · 2015
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Interaction networks for learning about objects, relations and physics
Battaglia, P. W., Pascanu, R., Lai, M., Rezende, D., and Kavukcuoglu, K. (2016) · 2016
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P. (2016) · 2016
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Prediction errors of molecular machine learning models lower than hybrid DFT error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., and von Lilienfeld, O. A. (2017) · 2017
Later among the works it cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E. (2017) · 2017
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Machine learning predictions of molecular properties: Accurate Many-Body potentials and nonlocality in chemical space
Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., and Tkatchenko, A. (2015) · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2015) · 2015
Cited alongside, same era.
The open quantum materials database (OQMD): assessing the accuracy of DFT formation energies
Kirklin, S., Saal, J. E., Meredig, B., Thompson, A., Doak, J. W., Aykol, M., Rühl, S., and Wolverton, C. (2015) · 2015
Cited alongside, same era.
Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., and Tkatchenko, A. (2017a)
Cited in the paper.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. T., Kindermans, P.-J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R. (2017b)
Cited in the paper.
SchNet - a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., and Müller, K.-R. (2017c)
Cited in the paper.
Ward, L., Liu, R., Krishna, A., Hegde, V. I., Agrawal, A., Choudhary, A., and Wolverton, C. (2017) · 2017
Later among the works it cites.
Xie, T. and Grossman, J. C. (2018) · 2018
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