AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification

Muhammed Shuaibi; Yuge Hu; Xiangyun Lei; Benjamin M. Comer; Matt Adams; Jacob Paras; Rui Qi Chen; Eric Musa; Joseph Musielewicz; Andrew A. Peterson; Andrew J. Medford; Zachary Ulissi

doi:10.21105/joss.05035

Review

Editor: @dhhagan (all papers)
Reviewers: @ml-evs (all reviews), @ianfhunter (all reviews)

Authors

Muhammed Shuaibi, Yuge Hu (0000-0003-3648-7749), Xiangyun Lei, Benjamin M. Comer, Matt Adams, Jacob Paras, Rui Qi Chen, Eric Musa, Joseph Musielewicz, Andrew A. Peterson (0000-0003-2855-9482), Andrew J. Medford (0000-0001-8311-9581), Zachary Ulissi (0000-0002-9401-4918)

Citation

Shuaibi et al., (2023). AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification. Journal of Open Source Software, 8(87), 5035, https://doi.org/10.21105/joss.05035

@article{Shuaibi2023, doi = {10.21105/joss.05035}, url = {https://doi.org/10.21105/joss.05035}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {87}, pages = {5035}, author = {Muhammed Shuaibi and Yuge Hu and Xiangyun Lei and Benjamin M. Comer and Matt Adams and Jacob Paras and Rui Qi Chen and Eric Musa and Joseph Musielewicz and Andrew A. Peterson and Andrew J. Medford and Zachary Ulissi}, title = {AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification}, journal = {Journal of Open Source Software} }

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