Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 13, Issue 21, Pages 4729-4738Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.2c01064
Keywords
-
Categories
Funding
- National Natural Science Foundation of China [21973009]
- Chongqing Municipal Natural Science Foundation [cstc2019jcyj-msxmX0087]
- Venture and Innovation Support Program for Chongqing Overseas Returnees [cx2021071]
Ask authors/readers for more resources
D-machine learning is a highly efficient method that improves the fit of potential energy surfaces using a small number of high-level energies. This study proposes a neural-network based approach to efficiently construct accurate potential energy surfaces for complex reactions.
D-machine learning, or the hierarchical construction scheme, is a highly cost-effective method, as only a small number of high-level ab initio energies are required to improve a potential energy surface (PES) fit to a large number of low-level points. However, there is no efficient and systematic way to select as few points as possible from the low-level data set. We here propose a permutation-invariant-polynomial neural-network (PIP-NN)based Delta-machine learning approach to construct full-dimensional accurate PESs of complicated reactions efficiently. Particularly, the high flexibility of the NN is exploited to efficiently sample points from the low-level data set. This approach is applied to the challenging case of a HO2 self-reaction with a large configuration space. Only 14% of the DFT data set is used to successfully bring a newly fitted DFT PES to the UCCSD(T)-F12a/AVTZ quality. Then, the quasiclassical trajectory (QCT) calculations are performed to study its dynamics, particularly the mode specificity.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available