E3S Web Conf.
Volume 267, 20217th International Conference on Energy Science and Chemical Engineering (ICESCE 2021)
|Number of page(s)||7|
|Section||Environmental Chemistry Research and Chemical Preparation Process|
|Published online||04 June 2021|
- Ulissi, Z. W., Medford, A. J., Bligaard, T., Nørskov, J. K. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nature communications, 8, 1, 1–7. (2017). [Google Scholar]
- Faber, F. A., Hutchison, L., Huang, B., et al. Prediction errors of molecular machine learning models lower than hybrid DFT error. Journal of chemical theory and computation, 13, 11, 5255–5264. (2017). [Google Scholar]
- Shen, L., & Yang, W. Molecular dynamics simulations with quantum mechanics/molecular mechanics and adaptive neural networks. Journal of chemical theory and computation, 14, 3, 1442–1455. (2018). [Google Scholar]
- Rosenbrock, C. W., Homer, E. R., Csányi, G., Hart, G. L. Discovering the building blocks of atomic systems using machine learning: application to grain boundaries. NPJ Computational Materials, 3,1, 1–7. (2017). [Google Scholar]
- Behler, J. Representing potential energy surfaces by high-dimensional neural network potentials. Journal of Physics: Condensed Matter, 26, 18, 183001. (2014). [Google Scholar]
- Wong, S. Y., Bund, R. K., Connelly, R. K., et al. Modeling the crystallization kinetic rates of lactose via artificial neural network. Crystal growth & design, 10, 6, 2620–2628. (2010). [Google Scholar]
- Bartók, A. P., Csányi, G. G aussian approximation potentials: A brief tutorial introduction. International Journal of Quantum Chemistry, 115, 16, 1051–1057. (2015). [Google Scholar]
- Bartók, A. P., Payne, M. C., Kondor, R., et al. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons. Physical review letters, 104, 13, 136403. (2010). [Google Scholar]
- Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., et al. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. The journal of physical chemistry letters, 6, 12, 2326–2331. (2015). [Google Scholar]
- Schütt, K. T., Arbabzadah, F., Chmiela, S., et al. Quantum-chemical insights from deep tensor neural networks. Nature communications, 8, 1, 1–8. (2017). [Google Scholar]
- Yao, K., Herr, J. E., Toth, D. W., et al. The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. Chemical science, 9, 8, 2261–2269. (2018). [Google Scholar]
- Huang, S. D., Shang, C., Kang, P. L., et al. Atomic structure of boron resolved using machine learning and global sampling. Chemical science, 9, 46,, 8644–8655. (2018). [Google Scholar]
- Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, 4, 2, 268–276. (2018). [Google Scholar]
- Ramprasad, R., Batra, R., Pilania, G., et al. Machine learning in materials informatics: recent applications and prospects. npj Computational Materials, 3, 1, 1–13. (2017). [Google Scholar]
- Shapeev, A. V. Applications of machine learning for representing interatomic interactions. In Computational Materials Discovery. Royal Society of Chemistry. (2018). [Google Scholar]
- Behler, J. Perspective: Machine learning potentials for atomistic simulations. The Journal of chemical physics, 145, 17, 170901. (2016). [Google Scholar]
- Xie, T., Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical review letters, 120, 14, 145301.1-145301.6. (2018). [Google Scholar]
- Khorshidi, A., Peterson, A. A. Amp: A modular approach to machine learning in atomistic simulations. Computer Physics Communications, 207, 310–324. (2016). [Google Scholar]
- Bartók, A. P., Kondor, R., Csányi, G. On representing chemical environments. Physical Review B, 87, 18, 184115. (2013). [Google Scholar]
- Imbalzano, G., Anelli, A., Giofré, D., et al. Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials. The Journal of chemical physics, 148, 24, 241730. (2018). [Google Scholar]
- Zhang, K., Yin, L., Liu, G. Physically inspired atomcentered symmetry functions for the construction of high dimensional neural network potential energy surfaces. Computational Materials Science, 186, 110071. (2021). [Google Scholar]
- Huo, H., Rupp, M. Unified representation of molecules and crystals for machine learning. arXiv preprint arXiv:1704.06439. (2017). [Google Scholar]
- Kim, H., Park, J. Y., Choi, S. Energy refinement and analysis of structures in the QM9 database via a highly accurate quantum chemical method. Scientific data, 6, 1, 1–8. (2019). [Google Scholar]
- Zeledon, J. A. H., Romero, A. H., Ren, P., et al. The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction. Journal of Applied Physics, 127, 21, 215108. (2020). [Google Scholar]
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