Towards fully ab initio simulation of atmospheric aerosol nucleation
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DOI: 10.1038/s41467-022-33783-y
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- Justin S. Smith & Benjamin T. Nebgen & Roman Zubatyuk & Nicholas Lubbers & Christian Devereux & Kipton Barros & Sergei Tretiak & Olexandr Isayev & Adrian E. Roitberg, 2019. "Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
- João Almeida & Siegfried Schobesberger & Andreas Kürten & Ismael K. Ortega & Oona Kupiainen-Määttä & Arnaud P. Praplan & Alexey Adamov & Antonio Amorim & Federico Bianchi & Martin Breitenlechner & And, 2013. "Molecular understanding of sulphuric acid–amine particle nucleation in the atmosphere," Nature, Nature, vol. 502(7471), pages 359-363, October.
- Jinzhe Zeng & Liqun Cao & Mingyuan Xu & Tong Zhu & John Z. H. Zhang, 2020. "Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
- Katrianne Lehtipalo & Linda Rondo & Jenni Kontkanen & Siegfried Schobesberger & Tuija Jokinen & Nina Sarnela & Andreas Kürten & Sebastian Ehrhart & Alessandro Franchin & Tuomo Nieminen & Francesco Ric, 2016. "The effect of acid–base clustering and ions on the growth of atmospheric nano-particles," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
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