[83] Felix A. Faber, Anders S. Christensen, Bing Huang, and O. Anatole Von Lilienfeld. Alchemical and structural distribution based representation for universal quantum machine learning. J. Chem. Phys., 148(24):241717, June 2018.
[84] Yaolong Zhang, Junfan Xia, and Bin Jiang. REANN: A PyTorch-based end-to-end multifunctional deep neural network package for molecular, reactive, and periodic systems. J. Chem. Phys., 156(11):114801, 2022.
[85] Bingqing Cheng, Edgar A. Engel, Jörg Behler, Christoph Dellago, and Michele Ceriotti. Ab initio thermodynamics of liquid and solid water. Proc. Natl. Acad. Sci. U. S. A., 116(4):1110-1115, January 2019.
[86] Johannes Gasteiger, Shankari Giri, Johannes T Margraf, and Stephan Gunnemann. Fast and uncertainty-aware directional message passing for non-equilibrium molecules. arXiv preprint arXiv:2011.14115, 2020.
[87] Johannes Klicpera, Janek Groß, and Stephan Gümnmann. Directional Message Passing for Molecular Graphs. 2020.
[88] Marco Eckhoff and Jörg Behler. High-dimensional neural network potentials for magnetic systems using spin-dependent atom-centered symmetry functions. npj Computational Materials, 7(1):170, 2021.
[89] James P Darby, David P Kovács, Ilyes Batatia, Miguel A Caro, Gus LW Hart, Christoph Ortner, and Gábor Csányi. Tensor-reduced atomic density representations. arXiv preprint arXiv:2210.01705, 2022.
[90] Nataliya Lopanitsyna, Guillaume Fraux, Maximilian A. Springer, Sandip De, and Michele Ceriotti. Modeling high-entropy transition metal alloys with alchemical compression. Phys. Rev. Materials, 7(4):045802, April 2023.
[91] Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole Von Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data, 1:1-7, August 2014.
[92] Larry Zitnick, Abhishek Das, Adeesh Kolluru, Janice Lan, Muhammed Shuaibi, Anuroop Sriram, Zachary Ulissi, and Brandon Wood. Spherical channels for modeling atomic interactions. Advances in Neural Information Processing Systems, 35:8054-8067, 2022.
[93] Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, and C Lawrence Zitnick. Forcenet: A graph neural network for large-scale quantum calculations. arXiv preprint arXiv:2103.01436, 2021.
[94] Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, et al. Open catalyst 2020 (oc20) dataset and community challenges. *Acs Catalysis*, 11(10):6059-6072, 2021.
[95] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015.
[96] Stefan Elfwing, Eiji Uchibe, and Kenji Doya. Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks, 107:3-11, 2018.
[97] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[98] Scipy generator of random rotations. https://docs.scipy.org/doc/scipy/reference/generated/scipy/spatial.transformRotation.random.html.
[99] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.