Vazquez, Guillermo, Singh, Prashant, Sauceda, Daniel, Couperthwaite, Richard, Britt, Nicholas, Youssef, Khaled, Johnson, Duane D., and Arróyave, Raymundo. Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys. Retrieved from https://par.nsf.gov/biblio/10344203. Acta Materialia 232.C Web. doi:10.1016/j.actamat.2022.117924.

Vazquez, Guillermo, Singh, Prashant, Sauceda, Daniel, Couperthwaite, Richard, Britt, Nicholas, Youssef, Khaled, Johnson, Duane D., & Arróyave, Raymundo. Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys. Acta Materialia, 232 (C). Retrieved from https://par.nsf.gov/biblio/10344203. https://doi.org/10.1016/j.actamat.2022.117924

Vazquez, Guillermo, Singh, Prashant, Sauceda, Daniel, Couperthwaite, Richard, Britt, Nicholas, Youssef, Khaled, Johnson, Duane D., and Arróyave, Raymundo. "Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys". Acta Materialia 232 (C). Country unknown/Code not available. https://doi.org/10.1016/j.actamat.2022.117924. https://par.nsf.gov/biblio/10344203.

@article{osti_10344203,
place = {Country unknown/Code not available}, title = {Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys}, url = {https://par.nsf.gov/biblio/10344203}, DOI = {10.1016/j.actamat.2022.117924}, abstractNote = {}, journal = {Acta Materialia}, volume = {232}, number = {C}, author = {Vazquez, Guillermo and Singh, Prashant and Sauceda, Daniel and Couperthwaite, Richard and Britt, Nicholas and Youssef, Khaled and Johnson, Duane D. and Arróyave, Raymundo}, }