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An atomic-energy based deep-learning neural network (NN) is used to represent the PES function, and the weighted atom-centered symmetry functions are employed as inputs of the NN model to satisfy the translational, rotational, and permutational symmetries, and to capture the geometry features of each atom and its individual chemical environment. Several standard technical tricks are used in the construction of NN-PES, which includes the application of clustering algorithm in the formation of the training dataset, the examination of the reliability of the NN-PES model by different fitted NN models, and the detection of the out-of-confidence region by the confidence interval of the training dataset. The accuracy of the full-dimensional NN-PES model is examined by two benchmark calculations with respect to ab initio data. Both the NN and electronic-structure calculations give a similar H-atom dissociation reaction pathway on the T1 state in the intrinsic reaction coordinate analysis. The small-scaled trial dynamics simulations based on NN-PES and ab initio PES give highly consistent results. After confirming the accuracy of the NN-PES, a large number of trajectories are calculated in the quasi-classical dynamics, which allows us to get a better understanding of the T1-driven H-atom dissociation dynamics efficiently. Particularly, the dynamics simulations from different initial conditions can be easily simulated with a rather low computational cost. The influence of the mode-specific vibrational excitations on the H-atom dissociation dynamics driven by the T1 state is explored. The results show that the vibrational excitations on symmetric C\u2013H stretching, asymmetric C\u2013H stretching, and C=O stretching motions always enhance the H-atom dissociation probability obviously.<\/jats:p>","DOI":"10.1063\/5.0067176","type":"journal-article","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T14:35:56Z","timestamp":1636554956000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":8,"title":["Theoretical studies on triplet-state driven dissociation of formaldehyde by quasi-classical molecular dynamics simulation on machine-learning potential energy surface"],"prefix":"10.1063","volume":"155","author":[{"given":"Shichen","family":"Lin","sequence":"first","affiliation":[{"name":"Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education 1 , South China Normal University, Guangzhou 510006, People's Republic of China"},{"name":"School of Chemistry, South China Normal University 2 , Guangzhou 510006, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daoling","family":"Peng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education 1 , South China Normal University, Guangzhou 510006, People's Republic of China"},{"name":"School of Chemistry, South China Normal University 2 , Guangzhou 510006, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5576-2828","authenticated-orcid":false,"given":"Weitao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Duke University 3 , Durham, North Carolina 27708, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8141-6774","authenticated-orcid":false,"given":"Feng Long","family":"Gu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education 1 , South China Normal University, Guangzhou 510006, People's Republic of China"},{"name":"School of Chemistry, South China Normal University 2 , Guangzhou 510006, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8509-0388","authenticated-orcid":false,"given":"Zhenggang","family":"Lan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Theoretical Chemistry of Environment, Ministry of Education 1 , South China Normal University, Guangzhou 510006, People's Republic of China"},{"name":"Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety; SCNU Environmental Research Institute; School of Environment, South China Normal University 4 , Guangzhou 510006, People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2021,12,7]]},"reference":[{"issue":"1","key":"2023062610395482500_c1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/a:1006127516791","article-title":"Biogenic volatile organic compounds (VOC): An overview on emission, physiology and ecology","volume":"33","year":"1999","journal-title":"J. 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