JAX-ReaxFF: A Gradient-Based Framework for Fast Optimization of Reactive Force Fields. Kaymak, M. C., Rahnamoun, A., O’Hearn, K. A., van Duin, A. C. T., Merz, K. M., & Aktulga, H. M. Journal of Chemical Theory and Computation, August, 2022.
JAX-ReaxFF: A Gradient-Based Framework for Fast Optimization of Reactive Force Fields [link]Paper  doi  bibtex   
@article{kaymak_jax-reaxff_2022,
	title = {{JAX}-{ReaxFF}: {A} {Gradient}-{Based} {Framework} for {Fast} {Optimization} of {Reactive} {Force} {Fields}},
	issn = {1549-9618, 1549-9626},
	shorttitle = {{JAX}-{ReaxFF}},
	url = {https://pubs.acs.org/doi/10.1021/acs.jctc.2c00363},
	doi = {10.1021/acs.jctc.2c00363},
	language = {en},
	urldate = {2022-08-22},
	journal = {Journal of Chemical Theory and Computation},
	author = {Kaymak, Mehmet Cagri and Rahnamoun, Ali and O’Hearn, Kurt A. and van Duin, Adri C. T. and Merz, Kenneth M. and Aktulga, Hasan Metin},
	month = aug,
	year = {2022},
	pages = {acs.jctc.2c00363},
}

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