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Machine learning potentials for complex aqueous systems made simple.
Schran C, Thiemann FL, Rowe P, Müller EA, Marsalek O, Michaelides A. Schran C, et al. Proc Natl Acad Sci U S A. 2021 Sep 21;118(38):e2110077118. doi: 10.1073/pnas.2110077118. Proc Natl Acad Sci U S A. 2021. PMID: 34518232 Free PMC article.
Tracking single adatoms in liquid in a transmission electron microscope.
Clark N, Kelly DJ, Zhou M, Zou YC, Myung CW, Hopkinson DG, Schran C, Michaelides A, Gorbachev R, Haigh SJ. Clark N, et al. Among authors: schran c. Nature. 2022 Sep;609(7929):942-947. doi: 10.1038/s41586-022-05130-0. Epub 2022 Jul 27. Nature. 2022. PMID: 35896149
Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer.
Schran C, Brieuc F, Marx D. Schran C, et al. J Chem Phys. 2021 Feb 7;154(5):051101. doi: 10.1063/5.0035438. J Chem Phys. 2021. PMID: 33557570
A previously published neural network potential for the description of protonated water clusters up to the protonated water tetramer, H(+)(H(2)O)(4), at an essentially converged coupled cluster accuracy [C. Schran, J. Behler, and D. Marx, J. Chem. Theory Comput. 16, …
A previously published neural network potential for the description of protonated water clusters up to the protonated water tetramer, H(+)(H …
27 results