Machine learning and data science in materials design: a themed collection

被引：4
|
作者：
Ferguson, Andrew [1 ,2 ,3 ]
Hachmann, Johannes [4 ,5 ,6 ]
机构：
[1] Univ Illinois, Dept Mat Sci & Engn, 1304 West Green St, Urbana, IL 61801 USA
[2] Univ Illinois, Dept Chem & Biomol Engn, 600 South Mathews Ave, Urbana, IL 61801 USA
[3] Univ Illinois, Dept Phys, 1110 West Green St, Urbana, IL 61801 USA
[4] SUNY Buffalo, Dept Chem & Biol Engn, Buffalo, NY 14260 USA
[5] New York State Ctr Excellence Mat Informat, Buffalo, NY 14203 USA
[6] SUNY Buffalo, Computat & Data Enabled Sci & Engn Grad Program, Buffalo, NY 14260 USA
来源：
MOLECULAR SYSTEMS DESIGN & ENGINEERING | 2018年 / 3卷 / 03期
关键词：
D O I：
10.1039/c8me90007h
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Guest Editors Andrew Ferguson and Johannes Hachmann introduce this themed collection of papers showcasing the latest research leveraging data science and machine learning approaches to guide the understanding and design of hard, soft, and biological materials with tailored properties, function and behaviour.
引用
收藏
页码：429 / 430
页数：2
相关论文
共 50 条
  • [1] Themed collection on Insightful Machine Learning for Physical Chemistry
    Clark, Aurora E.
    Dral, Pavlo O.
    Tamblyn, Isaac
    Isayev, Olexandr
    PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2023, 25 (34) : 22563 - 22564
  • [2] Big data and machine learning for materials science
    Rodrigues J.F., Jr.
    Florea L.
    de Oliveira M.C.F.
    Diamond D.
    Oliveira O.N., Jr.
    Discover Materials, 1 (1):
  • [3] Small data machine learning in materials science
    Xu, Pengcheng
    Ji, Xiaobo
    Li, Minjie
    Lu, Wencong
    NPJ COMPUTATIONAL MATERIALS, 2023, 9 (01)
  • [4] Small data machine learning in materials science
    Pengcheng Xu
    Xiaobo Ji
    Minjie Li
    Wencong Lu
    npj Computational Materials, 9
  • [5] Revolutionizing Energetic Materials Discovery and Design: The Role of Data Science and Machine Learning
    Gee, Richard
    Lindsey, Rebecca
    PROPELLANTS EXPLOSIVES PYROTECHNICS, 2023, 48 (04)
  • [6] Data quantity governance for machine learning in materials science
    Yue Liu
    Zhengwei Yang
    Xinxin Zou
    Shuchang Ma
    Dahui Liu
    Maxim Avdeev
    Siqi Shi
    National Science Review, 2023, 10 (07) : 234 - 250
  • [7] A data ecosystem to support machine learning in materials science
    Ben Blaiszik
    Logan Ward
    Marcus Schwarting
    Jonathon Gaff
    Ryan Chard
    Daniel Pike
    Kyle Chard
    Ian Foster
    MRS Communications, 2019, 9 : 1125 - 1133
  • [8] A data ecosystem to support machine learning in materials science
    Blaiszik, Ben
    Ward, Logan
    Schwarting, Marcus
    Gaff, Jonathon
    Chard, Ryan
    Pike, Daniel
    Chard, Kyle
    Foster, Ian
    MRS COMMUNICATIONS, 2019, 9 (04) : 1125 - 1133
  • [9] Machine-Learning Metacomputing for Materials Science Data
    Steuben, J. C.
    Geltmacher, A. B.
    Rodriguez, S. N.
    Birnbaum, A. J.
    Graber, B. D.
    Rawlings, A. K.
    Iliopoulos, A. P.
    Michopoulos, J. G.
    JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING, 2024, 24 (11)
  • [10] Editorial: Machine Learning and Data Mining in Materials Science
    Huber, Norbert
    Kalidindi, Surya R.
    Klusemann, Benjamin
    Cyron, Christian J.
    FRONTIERS IN MATERIALS, 2020, 7
12345