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In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
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2010
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2010
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arXiv: 1109.2618
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2014
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2015
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2016
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K. Schütt, P.-J. Kindermans, H. E. S. Felix, S. Chmiela, A. Tkatchenko, and K.-R. Müller, “SchNet: A continuous-filter convolutional neural network for modeling quantum interactions,” in Neural Information Processing Systems
2017
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S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, and K.-R. Müller, “Machine learning of accurate energy-conserving molecular force fields,” Science Advances
2017
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2017
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2017
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2020
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2018
Cited alongside, same era.
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, “SchNet - a deep learning architecture for molecules and materials,” The Journal of Chemical Physics
2018
Cited alongside, same era.
L. Zhang, J. Han, H. Wang, R. Car, and W. E, “Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics,” Physical Review Letters
2018
Cited alongside, same era.
K. T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, and K. R. Müller, “SchNetPack: A Deep Learning Toolbox For Atomistic Systems,” Journal of Chemical Theory and Computation
2018
Cited alongside, same era.
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, “Machine learning for molecular and materials science,” Nature
2018
Cited alongside, same era.
S. Chmiela, H. E. Sauceda, K.-R. Müller, and A. Tkatchenko, “Towards exact molecular dynamics simulations with machine-learned force fields,” Nature communications
2018
Cited alongside, same era.
M. Popova, O. Isayev, and A. Tropsha, “Deep reinforcement learning for de novo drug design,” Science advances
2018
Cited alongside, same era.
W. Jin, R. Barzilay, and T. Jaakkola, “Junction tree variational autoencoder for molecular graph generation,” in ICML
2018
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2020
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W. Jin, R. Barzilay, and T. Jaakkola, “Multi-objective molecule generation using interpretable substructures,” in International Conference on Machine Learning
2020
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B. Chen, T. Wang, C. Li, H. Dai, and L. Song, “Molecule optimization by explainable evolution,” in International Conference on Learning Representations
2020
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H. E. Webel, T. B. Kimber, S. Radetzki, M. Neuenschwander, M. Nazaré, and A. Volkamer, “Revealing cytotoxic substructures in molecules using deep learning,” Journal of computer-aided molecular design
2020
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F. M. Bianchi, D. Grattarola, and C. Alippi, “Spectral clustering with graph neural networks for graph pooling,” in International conference on machine learning
2020
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B. E. Husic, N. E. Charron, D. Lemm, J. Wang, A. Pérez, M. Majewski, A. Krämer, Y. Chen, S. Olsson, G. de Fabritiis, et al
2020
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Publisher: American Institute of Physics
J. Wang, S. Chmiela, K.-R. Müller, F. Noé, and C. Clementi, “Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach,” The Journal of Chemical Physics · 2020
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K. Schütt, O. Unke, and M. Gastegger, “Equivariant message passing for the prediction of tensorial properties and molecular spectra,” in Proceedings of the 38th International Conference on Machine Learning
2021
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O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Schütt, A. Tkatchenko, and K.-R. Müller, “Machine learning force fields,” Chemical Reviews
2021
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O. T. Unke, S. Chmiela, M. Gastegger, K. T. Schütt, H. E. Sauceda, and K.-R. Müller, “SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects,” Nature Communications
2021
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J. A. Keith, V. Vassilev-Galindo, B. Cheng, S. Chmiela, M. Gastegger, K.-R. Müller, and A. Tkatchenko, “Combining machine learning and computational chemistry for predictive insights into chemical systems,” Chem. Rev
2021
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S. Doerr, M. Majewski, A. Pérez, A. Kramer, C. Clementi, F. Noe, T. Giorgino, and G. De Fabritiis, “Torchmd: A deep learning framework for molecular simulations,” Journal of chemical theory and computation
2021
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B. Huang and O. A. Von Lilienfeld, “Ab initio machine learning in chemical compound space,” Chemical reviews
2021
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T. S. Hy and R. Kondor, “Multiresolution graph variational autoencoder,” 2021
2021
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W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. Müller, “Explaining deep neural networks and beyond: A review of methods and applications,” Proceedings of the IEEE
2021
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PMID: 33872000
A. Mukherjee, A. Su, and K. Rajan, “Deep learning model for identifying critical structural motifs in potential endocrine disruptors,” Journal of Chemical Information and Modeling · 2021
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2021
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L. I. Vazquez-Salazar, E. Boittier, O. T. Unke, and M. Meuwly, “Impact of the characteristics of quantum chemical databases on machine learning predictions of tautomerization energies,” Journal of Chemical Theory and Computation
2021
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T. D. Potter, E. L. Barrett, and M. A. Miller, “Automated coarse-grained mapping algorithm for the martini force field and benchmarks for membrane–water partitioning,” Journal of Chemical Theory and Computation
2021
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M. Gastegger, K. T. Schütt, and K.-R. Müller, “Machine learning of solvent effects on molecular spectra and reactions,” Chemical science
2021
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S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky, “E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,” Nature communications
2022
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N. W. Gebauer, M. Gastegger, S. S. Hessmann, K.-R. Müller, and K. T. Schütt, “Inverse design of 3d molecular structures with conditional generative neural networks,” Nature Communications
2022
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T. Schnake, O. Eberle, J. Lederer, S. Nakajima, K. T. Schütt, K.-R. Müller, and G. Montavon, “Higher-order explanations of graph neural networks via relevant walks,” IEEE transactions on pattern analysis and machine intelligence
2022
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S. Letzgus, P. Wagner, J. Lederer, W. Samek, K.-R. Müller, and G. Montavon, “Toward explainable artificial intelligence for regression models: A methodological perspective,” IEEE Signal Processing Magazine
2022
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S. Chmiela, V. Vassilev-Galindo, O. T. Unke, A. Kabylda, H. E. Sauceda, A. Tkatchenko, and K.-R. Müller, “Accurate global machine learning force fields for molecules with hundreds of atoms,” Science Advances
2023
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A. Musaelian, S. Batzner, A. Johansson, L. Sun, C. J. Owen, M. Kornbluth, and B. Kozinsky, “Learning local equivariant representations for large-scale atomistic dynamics,” Nature Communications
2023
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