Fall_2023_Deep_Learning_project_submission_-_Nofit_Segal

assets/bibliography/2023-11-08-A-deeper-look-into-equivariance-for-materials-data.bib

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+@article{min2020accelerated,
+  title={Accelerated discovery of novel inorganic materials with desired properties using active learning},
+  author={Min, Kyoungmin and Cho, Eunseog},
+  journal={The Journal of Physical Chemistry C},
+  volume={124},
+  number={27},
+  pages={14759--14767},
+  year={2020},
+  publisher={ACS Publications}
+}
+@article{merchant2023scaling,
+  title={Scaling deep learning for materials discovery},
+  author={Merchant, Amil and Batzner, Simon and Schoenholz, Samuel S and Aykol, Muratahan and Cheon, Gowoon and Cubuk, Ekin Dogus},
+  journal={Nature},
+  pages={1--6},
+  year={2023},
+  publisher={Nature Publishing Group UK London}
+}
+@article{pyzer2022accelerating,
+  title={Accelerating materials discovery using artificial intelligence, high performance computing and robotics},
+  author={Pyzer-Knapp, Edward O and Pitera, Jed W and Staar, Peter WJ and Takeda, Seiji and Laino, Teodoro and Sanders, Daniel P and Sexton, James and Smith, John R and Curioni, Alessandro},
+  journal={npj Computational Materials},
+  volume={8},
+  number={1},
+  pages={84},
+  year={2022},
+  publisher={Nature Publishing Group UK London}
+}
+@article{schutt2018schnet,
+  title={Schnet--a deep learning architecture for molecules and materials},
+  author={Sch{\"u}tt, Kristof T and Sauceda, Huziel E and Kindermans, P-J and Tkatchenko, Alexandre and M{\"u}ller, K-R},
+  journal={The Journal of Chemical Physics},
+  volume={148},
+  number={24},
+  year={2018},
+  publisher={AIP Publishing}
+}
+@article{batzner20223,
+  title={E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials},
+  author={Batzner, Simon and Musaelian, Albert and Sun, Lixin and Geiger, Mario and Mailoa, Jonathan P and Kornbluth, Mordechai and Molinari, Nicola and Smidt, Tess E and Kozinsky, Boris},
+  journal={Nature communications},
+  volume={13},
+  number={1},
+  pages={2453},
+  year={2022},
+  publisher={Nature Publishing Group UK London}
+}
+@article{chmiela2019sgdml,
+  title={sGDML: Constructing accurate and data efficient molecular force fields using machine learning},
+  author={Chmiela, Stefan and Sauceda, Huziel E and Poltavsky, Igor and M{\"u}ller, Klaus-Robert and Tkatchenko, Alexandre},
+  journal={Computer Physics Communications},
+  volume={240},
+  pages={38--45},
+  year={2019},
+  publisher={Elsevier}
+}
+@article{DBLP:journals/corr/abs-1905-04943,
+  author       = {Nicolas Keriven and
+                  Gabriel Peyr{\'{e}}},
+  title        = {Universal Invariant and Equivariant Graph Neural Networks},
+  journal      = {CoRR},
+  volume       = {abs/1905.04943},
+  year         = {2019},
+  url          = {http://arxiv.org/abs/1905.04943},
+  eprinttype    = {arXiv},
+  eprint       = {1905.04943},
+  timestamp    = {Sun, 25 Jul 2021 11:39:06 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-1905-04943.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}