MaterialNet: A web-based graph explorer for materials science data. - Roni Choudhury; Muratahan Aykol; Samuel Gratzl; Joseph H. Montoya; Jens S. Hummelshøj (J. Open Source Softw., 2020)

Closed-loop optimization of fast-charging protocols for batteries with machine learning. - Peter M. Attia; Aditya Grover; Norman Jin; Kristen A. Severson; Todor M. Markov; Yang-Hung Liao; Michael H. Chen; Bryan Cheong; Nicholas Perkins; Zi Yang; Patrick K. Herring; Muratahan Aykol; et al. (Nat., 2020)

BEEP: A Python library for Battery Evaluation and Early Prediction. - Patrick K. Herring; Chirranjeevi Balaji Gopal; Muratahan Aykol; Joseph H. Montoya; Abraham Anapolsky; Peter M. Attia; William Gent; Jens S. Hummelshøj; Linda Hung; Ha-Kyung Kwon; Patrick Moore; Daniel Schweigert; et al. (SoftwareX, 2020)

LixCoO2 phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories. - Gregory H. Teichert; Sambit Das; Muratahan Aykol; Chirranjeevi Balaji Gopal; Vikram Gavini; Krishna C. Garikipati (CoRR, 2021)

Predicting emergence of crystals from amorphous matter with deep learning. - Muratahan Aykol; Amil Merchant; Simon L. Batzner; Jennifer N. Wei; Ekin Dogus Cubuk (CoRR, 2023)

Scaling deep learning for materials discovery. - Amil Merchant; Simon L. Batzner; Samuel S. Schoenholz; Muratahan Aykol; Gowoon Cheon; Ekin Dogus Cubuk (Nat., 2023)

Generative Hierarchical Materials Search. - Sherry Yang 001; Simon L. Batzner; Ruiqi Gao; Muratahan Aykol; Alexander L. Gaunt; Brendan McMorrow; Danilo Jimenez Rezende; Dale Schuurmans; Igor Mordatch; Ekin Dogus Cubuk (NeurIPS, 2024)

Predicting emergence of crystals from amorphous precursors with deep learning potentials. - Muratahan Aykol; Amil Merchant; Simon L. Batzner; Jennifer N. Wei; Ekin Dogus Cubuk (Nat. Comput. Sci., 2025)