The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial - neural network approach
Abstract
With advances in ab initio theory, it is now possible to calculate electronic energies within chemical (<1 kcal/mol) accuracy. However, it is still challenging to represent faithfully a large number of ab initio points with a multidimensional analytical function over a large configuration space, which is needed for accurate dynamical studies. In this Review, we discuss our recent work on a new potential-fitting approach based on artificial neural networks, which are ultra-flexible in representing any multidimensional real functions. A unique feature of our neural network approach is how the symmetries, particularly those associated with the exchange of identical atoms in the system, are enforced. To this end, symmetry functions in the form of symmetrised monomials that satisfy a particular type of symmetry possessed by the system are used in the input layer of the neural network. This approach is rigorous, accurate, and efficient. It is also simple to implement, requiring no modification of the neural network routines. Its applications to the construction of multi-dimensional potential energy surfaces in many gas phase and gas–surface systems as surveyed here.
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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
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
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
Metadata
- Authors
- Jiang, B., Li, J., & Guo, H.
- Venue
- International Reviews in Physical Chemistry
- Publisher
- Informa UK Limited
- Published
- 2016-07-02
- Volume
- 35
- Issue
- 3
- Pages
- 479-506
- ISSN
- 0144-235X (ISSN-L)
- 1366-591X
- OpenAlex
- W2508146432
- Cited by
- 401
- References
- 88
- FWCI
- 20.00
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jiang, B., Li, J., & Guo, H. (2016). Potential energy surfaces from high fidelity fitting of ab initio points: the permutation invariant polynomial - neural network approach. International Reviews in Physical Chemistry. https://doi.org/10.1080/0144235x.2016.1200347
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