.png){kind=icon}
返回
Comparing machine learning techniques for predicting glassy dynamics
.png){kind=icon}
OA
AI
DOI：10.1063/5.0088581.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the quest to understand how structure and dynamics are connected in glasses, a number of machine learning based methods have been developed that predict dynamics in supercooled liquids. These methods include both increasingly complex machine learning techniques and increasingly sophisticated descriptors used to describe the environment around particles. In many cases, both the chosen machine learning technique and choice of structural descriptors are varied simultaneously, making it hard to quantitatively compare the performance of different machine learning approaches. Here, we use three different machine learning algorithms-linear regression, neural networks, and graph neural networks-to predict the dynamic propensity of a glassy binary hard-sphere mixture using as structural input a recursive set of order parameters recently introduced by Boattini et al. [Phys. Rev. Lett. 127, 088007 (2021)]. As we show, when these advanced descriptors are used, all three methods predict the dynamics with nearly equal accuracy. However, the linear regression is orders of magnitude faster to train, making it by far the method of choice.
Keyword:
RELAXATION
LIQUIDS
HIDDEN
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
机构
引用论文
.png){kind=icon}
Validity and time savings in the selection of short forms of the Wechsler Adult Intelligence Scale—Revised.修订后的韦氏成人智力量表简短形式选择的有效性和时间节省。
PREAI
分享收藏
查看更多内容