The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Communication: Fitting potential energy surfaces with fundamental invariant neural network
Open accessAbstract
A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energy surfaces for OH3 and CH4 were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.
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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
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Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
Yao, K., Herr, J. E., Brown, S. N., & Parkhill, J. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b01072 · Cited by 126
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Metadata
- Authors
- Shao, K., Chen, J., Zhao, Z., & Zhang, D. H.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2016-08-17
- Volume
- 145
- Issue
- 7
- Pages
- 071101-071101
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2511310272
- Cited by
- 258
- References
- 21
- FWCI
- 6.67
- Type
- journal article
- Language
- en
- Open access
- bronze
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