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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ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
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Self-citationEnumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
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Quantum-chemical insights from deep tensor neural networks
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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
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
Less is more: Sampling chemical space with active learning
Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O., & Roitberg, A. E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5023802 · Cited by 811
Self-citationA quantitative uncertainty metric controls error in neural network-driven chemical discovery
Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019) · Chemical Science · doi:10.1039/c9sc02298h · Cited by 243
Willatt, M. J., Musil, F., & Ceriotti, M. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp05921g · Cited by 141
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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