Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins

[Image: see text] We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and...

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Detalles Bibliográficos
Autores principales: Morado, João, Mortenson, Paul N., Nissink, J. Willem M., Essex, Jonathan W., Skylaris, Chris-Kriton
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170518/
https://www.ncbi.nlm.nih.gov/pubmed/37071825
http://dx.doi.org/10.1021/acs.jcim.2c01510

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10170518/
https://www.ncbi.nlm.nih.gov/pubmed/37071825
http://dx.doi.org/10.1021/acs.jcim.2c01510

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