Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
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DOI: 10.1038/s41467-022-30994-1
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- Weike Ye & Chi Chen & Zhenbin Wang & Iek-Heng Chu & Shyue Ping Ong, 2018. "Deep neural networks for accurate predictions of crystal stability," Nature Communications, Nature, vol. 9(1), pages 1-6, December.
- Dipendra Jha & Kamal Choudhary & Francesca Tavazza & Wei-keng Liao & Alok Choudhary & Carelyn Campbell & Ankit Agrawal, 2019. "Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
- Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
- 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.
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