The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Hierarchical modeling of molecular energies using a deep neural network
Open accessAbstract
We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.
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Cites
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Self-citationEnumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
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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
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
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Less is more: Sampling chemical space with active learning
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Self-citationA quantitative uncertainty metric controls error in neural network-driven chemical discovery
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Accurate large-scale simulations of siliceous zeolites by neural network potentials
Erlebach, A., Nachtigall, P., & Grajciar, L. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00865-w · Cited by 58
Machine learning potential era of zeolite simulation
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Numerical Solution of the Parametric Diffusion Equation by Deep Neural Networks
Geist, M., Petersen, P., Raslan, M., Schneider, R., & Kutyniok, G. (2021) · Journal of Scientific Computing · doi:10.1007/s10915-021-01532-w · Cited by 57
Enumeration of de novo inorganic complexes for chemical discovery and machine learning
Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/c9me00069k · Cited by 43
Metadata
- Authors
- Lubbers, N., Smith, J. S., & Barros, K.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2018-03-19
- Volume
- 148
- Issue
- 24
- Pages
- 241715-241715
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2761704992
- Cited by
- 306
- References
- 12
- FWCI
- 12.38
- Type
- journal article
- Language
- en
- Open access
- green
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