Muratahan Aykol

According to our database, Muratahan Aykol authored at least 6 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Scaling deep learning for materials discovery.

Amil Merchant, Simon L. Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, Ekin Dogus Cubuk

Nat., 2023

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

2021

Li_xCoO₂ 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

2020

BEEP: A Python library for Battery Evaluation and Early Prediction.

Patrick K. Herring, Chirranjeevi Balaji Gopal, Muratahan Aykol, Joseph Montoya, Abraham Anapolsky, Peter M. Attia, William Gent, Jens S. Hummelshøj, Linda Hung, Ha-Kyung Kwon, Patrick Moore, Daniel Schweigert, Kristen A. Severson, Santosh Suram, Zi Yang, Richard D. Braatz, Brian D. Storey

SoftwareX, 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, Stephen J. Harris, Richard D. Braatz, Stefano Ermon, William C. Chueh

Nat., 2020

MaterialNet: A web-based graph explorer for materials science data.

Roni Choudhury, Muratahan Aykol, Samuel Gratzl, Joseph Montoya, Jens S. Hummelshøj

J. Open Source Softw., 2020