Xuan, Yao, Delaney, Kris T., Ceniceros, Hector D., and Fredrickson, Glenn H. Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery. Retrieved from https://par.nsf.gov/biblio/10289292. Journal of Computational Physics 443.C Web. doi:10.1016/j.jcp.2021.110519.

Xuan, Yao, Delaney, Kris T., Ceniceros, Hector D., & Fredrickson, Glenn H. Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery. Journal of Computational Physics, 443 (C). Retrieved from https://par.nsf.gov/biblio/10289292. https://doi.org/10.1016/j.jcp.2021.110519

Xuan, Yao, Delaney, Kris T., Ceniceros, Hector D., and Fredrickson, Glenn H. "Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery". Journal of Computational Physics 443 (C). Country unknown/Code not available. https://doi.org/10.1016/j.jcp.2021.110519. https://par.nsf.gov/biblio/10289292.

@article{osti_10289292,
place = {Country unknown/Code not available}, title = {Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery}, url = {https://par.nsf.gov/biblio/10289292}, DOI = {10.1016/j.jcp.2021.110519}, abstractNote = {}, journal = {Journal of Computational Physics}, volume = {443}, number = {C}, author = {Xuan, Yao and Delaney, Kris T. and Ceniceros, Hector D. and Fredrickson, Glenn H.}, editor = {null} }