Advanced search

Filter By:

Journal Check one or more journals to show results from those journals only.

Choose more journals

Article type Check one or more article types to show results from those article types only.
Subject Check one or more subjects to show results from those subjects only.
Date Choose a date option to show results from those dates only.

Custom date range

Clear all filters
Sort by:
Showing 1–14 of 14 results
Advanced filters: Author: Muratahan Aykol Clear advanced filters

  • Degradation of cathode materials is a key factor hindering the long-term stability of lithium ion batteries. Here, the authors develop a high-throughput computational approach to design effective cathode coating materials, proposing a selection of candidate materials to help improve cathode lifetimes.

    • Muratahan Aykol
    • Soo Kim
    • C. Wolverton
    ResearchOpen Access
    Nature Communications
    Volume: 7, P: 1-12

  • Predicting the synthesizability of inorganic materials is challenging due to the many variables and complex phenomena involved in synthesis. Here, the authors combine material stabilities with a historical analysis of experimental discovery timelines as a temporal network to predict the synthesizability of hypothetical materials.

    • Muratahan Aykol
    • Vinay I. Hegde
    • Jens S. Hummelshøj
    ResearchOpen Access
    Nature Communications
    Volume: 10, P: 1-7

  • Batteries, as complex materials systems, pose unique challenges for the application of machine learning. Although a shift to data-driven, machine learning-based battery research has started, new initiatives in academia and industry are needed to fully exploit its potential.

    • Muratahan Aykol
    • Patrick Herring
    • Abraham Anapolsky
    Comments & Opinion
    Nature Reviews Materials
    Volume: 5, P: 725-727

  • This study introduces a2c, a computational method that leverages machine learning and atomistic simulations to predict the most likely crystallization products upon annealing of amorphous precursors. The a2c tool was demonstrated on a variety of materials, including oxides, nitrides and metallic glasses, and can assist researchers in discovering synthesis pathways for materials design.

    • Muratahan Aykol
    • Amil Merchant
    • Ekin Dogus Cubuk
    ResearchOpen Access
    Nature Computational Science
    Volume: 5, P: 105-111

  • A protocol using large-scale training of graph networks enables high-throughput discovery of novel stable structures and led to the identification of 2.2 million crystal structures, of which 381,000 are newly discovered stable materials.

    • Amil Merchant
    • Simon Batzner
    • Ekin Dogus Cubuk
    ResearchOpen Access
    Nature
    Volume: 624, P: 80-85

  • Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

    • Kristen A. Severson
    • Peter M. Attia
    • Richard D. Braatz
    Research
    Nature Energy
    Volume: 4, P: 383-391

  • A closed-loop machine learning methodology of optimizing fast-charging protocols for lithium-ion batteries can identify high-lifetime charging protocols accurately and efficiently, considerably reducing the experimental time compared to simpler approaches.

    • Peter M. Attia
    • Aditya Grover
    • William C. Chueh
    Research
    Nature
    Volume: 578, P: 397-402

  • The discovery and development of advanced materials are imperative for the clean energy sector. We envision that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.

    • Daniel P. Tabor
    • Loïc M. Roch
    • Alán Aspuru-Guzik
    Reviews
    Nature Reviews Materials
    Volume: 3, P: 5-20