Quantifying disorder one atom at a time using an interpretable graph neural network paradigm.

James Chapman, Tim Hsu, Xiao Chen, Tae Wook Heo, Brandon C Wood

July 2023 Nat Commun

Synopsis of Social media discussions

The collected discussions reflect a balanced curiosity about the interpretability and application of the graph neural network paradigm, with some posts describing how SODAS uniquely measures atomic disorder at a microscale, such as 'quantifying disorder atom by atom,' and others noting its usefulness in predicting material failure. The tone varies from technical appreciation to cautious optimism, highlighting both the innovative aspects and the need for further validation.

A
Agreement
 Neither agree nor disagree

The discussions show mixed reactions, with some support for the new method, but no strong consensus of agreement or disagreement.

I
Interest
 Moderate level of interest

Participants display a moderate level of curiosity about explaining atomic disorder and the GNN approach.

E
Engagement
 Moderate level of engagement

Comments include technical insights and contextual explanations, indicating engaged analysis.

I
Impact
 Moderate level of impact

The posts suggest recognition of the research's potential significance, but without emphasizing widespread transformative implications.

Social Mentions

YouTube

1 Videos

Twitter

1 Posts

Metrics

Video Views

18

Total Likes

5

Extended Reach

304

Social Features

2

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Measuring Atomic Disorder with Graph Neural Networks in Materials Science

Measuring Atomic Disorder with Graph Neural Networks in Materials Science

This video introduces SODAS, an interpretable metric using graph neural networks to quantify local atomic disorder, tracking variations from solid to liquid states and predicting material behavior.

STEM RTCL TV

October 21, 2023

18 views

  • kwh_rd100
    @KwhRd100 (Twitter)

    解釈可能なグラフ・ニューラル・ネットワークのパラダイムを用いた、原子1個ずつの無秩序の定量化　局所的な原子環境をグラフ表現に変換しGNNでエンコードしてから、これを局所順序パラメータSODASにマッピングして定量化する提案。固液界面の特性評価等で性能実証。実装あり。https://t.co/PmMxxctq1A
    view full post

    July 9, 2023

    5

Abstract Synopsis

  • This research introduces SODAS, a new, interpretable metric using graph neural networks to measure local atomic disorder in materials, capturing how atomic configurations vary from solid to liquid states.
  • The method is applied to different examples like grain boundaries, interfaces, microstructures, and fracture, demonstrating its ability to track how disorder evolves over time and space in materials.
  • By comparing SODAS with traditional methods, the study shows how it can help predict material behavior and failure, providing a clearer link between atomic-level details and large-scale material properties.]