The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
Open accessAbstract
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.
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Cites
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
Self-citationLarge Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
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Self-citationSolving the quantum many-body problem with artificial neural networks
Carleo, G., & Troyer, M. (2017) · Science · doi:10.1126/science.aag2302 · Cited by 2,215
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445
Self-citationKim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., & Sayres, R. (2017) · arXiv · doi:10.48550/arxiv.1711.11279 · Cited by 1,240
Comparing molecules and solids across structural and alchemical space
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Machine learning molecular dynamics for the simulation of infrared spectra
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Self-citationSymmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
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Cited by
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Self-citationArtificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“With these data, we can determine the electronic properties of molecules, the arrangement of chemical bonds around a molecule, and the location of reactive sites [78].”
BackgroundCombining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Self-citationDeep generative molecular design reshapes drug discovery
Zeng, X., Wang, F., Luo, Y., Kang, S.-G., Tang, J., Lightstone, F. C., Fang, E. F., Cornell, W., Nussinov, R., & Cheng, F. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100794 · Cited by 295
Jeyasubramanian, K., Thangagiri, B., Sakthivel, A., Dhaveethu Raja, J., Seenivasan, S., Vallinayagam, P., Madhavan, D., Malathi Devi, S., & Rathika, B. (2021) · Fuel · doi:10.1016/j.fuel.2021.120243 · Cited by 197
Metadata
- Authors
- Schütt, K. T., Gastegger, M., Tkatchenko, A., Müller, K.-R., & Maurer, R. J.
- Venue
- Nature Communications
- Published
- 2019-11-15
- Volume
- 10
- Issue
- 1
- Pages
- 5024-5024
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2984234582
- Cited by
- 476
- References
- 12
- FWCI
- 21.62
- Type
- journal article
- Language
- en
- Open access
- gold
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