Machine learning glasses

被引：6
|
作者：
Biroli, Giulio [1 ]
机构：
[1] Univ Paris, Sorbonne Univ, Univ PSL, Lab Phys,ENS,CNRS, Paris, France
来源：
NATURE PHYSICS | 2020年 / 16卷 / 04期
关键词：
4;
D O I：
10.1038/s41567-020-0873-1
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
Artificial neural networks now allow the dynamics of supercooled liquids to be predicted from their structure alone in an unprecedented way, thus providing a powerful new tool to study the physics of the glass transition.
引用
收藏
页码：373 / 374
页数：2
相关论文
共 50 条
  • [1] Machine learning glasses
    Giulio Biroli
    Nature Physics, 2020, 16 : 373 - 374
  • [2] Finding defects in glasses through machine learning
    Ciarella, Simone
    Khomenko, Dmytro
    Berthier, Ludovic
    Mocanu, Felix C.
    Reichman, David R.
    Scalliet, Camille
    Zamponi, Francesco
    NATURE COMMUNICATIONS, 2023, 14 (01)
  • [3] Finding defects in glasses through machine learning
    Simone Ciarella
    Dmytro Khomenko
    Ludovic Berthier
    Felix C. Mocanu
    David R. Reichman
    Camille Scalliet
    Francesco Zamponi
    Nature Communications, 14
  • [4] Learning molecular dynamics: predicting the dynamics of glasses by a machine learning simulator
    Liu, Han
    Huang, Zijie
    Schoenholz, Samuel S.
    Cubuk, Ekin D.
    Smedskjaer, Morten M.
    Sun, Yizhou
    Wang, Wei
    Bauchy, Mathieu
    MATERIALS HORIZONS, 2023, 10 (09) : 3416 - 3428
  • [5] NMR shifts in aluminosilicate glasses via machine learning
    Chaker, Ziyad
    Salanne, Mathieu
    Delaye, Jean-Marc
    Charpentier, Thibault
    PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2019, 21 (39) : 21709 - 21725
  • [6] A review on Machine learning aspect in physics and mechanics of glasses
    Singh, Jashanpreet
    Singh, Simranjit
    MATERIALS SCIENCE AND ENGINEERING B-ADVANCED FUNCTIONAL SOLID-STATE MATERIALS, 2022, 284
  • [7] The glass transition of CuZr metallic glasses in the perspective of machine learning
    Liu, Saihua
    Yang, Chengqiao
    Qi, Rui
    Sun, Minhua
    COMPUTATIONAL MATERIALS SCIENCE, 2024, 244
  • [8] Accelerated design of multicomponent metallic glasses using machine learning
    Anurag Bajpai
    Jatin Bhatt
    N. P. Gurao
    Krishanu Biswas
    Journal of Materials Research, 2022, 37 : 2428 - 2445
  • [9] Machine learning modeling for the prediction of plastic properties in metallic glasses
    Nicolás Amigo
    Simón Palominos
    Felipe J. Valencia
    Scientific Reports, 13
  • [10] Machine learning modeling for the prediction of plastic properties in metallic glasses
    Amigo, Nicolas
    Palominos, Simon
    Valencia, Felipe J.
    SCIENTIFIC REPORTS, 2023, 13 (01)
12345