Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation
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DOI: 10.1038/s41467-020-19497-z
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Cited by:
- Wang, Xueyan & Tian, Hua & Shu, Gequn & Yang, Zhao, 2024. "Study on flammability limit and combustion reactions behaviors of R744/R152a environmentally friendly mixed working fluid by experiments and molecular dynamic simulation," Energy, Elsevier, vol. 304(C).
- Shuai Jiang & Yi-Rong Liu & Teng Huang & Ya-Juan Feng & Chun-Yu Wang & Zhong-Quan Wang & Bin-Jing Ge & Quan-Sheng Liu & Wei-Ran Guang & Wei Huang, 2022. "Towards fully ab initio simulation of atmospheric aerosol nucleation," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
- Hanwen Zhang & Veronika Juraskova & Fernanda Duarte, 2024. "Modelling chemical processes in explicit solvents with machine learning potentials," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
- Jing Wu & E Zhou & An Huang & Hongbin Zhang & Ming Hu & Guangzhao Qin, 2024. "Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
- Jonathan Vandermause & Yu Xie & Jin Soo Lim & Cameron J. Owen & Boris Kozinsky, 2022. "Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
- Sunghwan Choi, 2023. "Prediction of transition state structures of gas-phase chemical reactions via machine learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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