在合金中使用图形神经网络和可解释的人工智能进行结构属性链接
Benjamin Rhoads1, Abigail Hogue1, Lars Kotthoff2
1Department of Mechanical Engineering, University of Mississippi, University, MS 38677, USA.
Materials (Basel, Switzerland)
|August 28, 2025
概括
图形神经网络 (GNN) 从微结构中有效预测Ni-Al合金的机械性能,优于卷积神经网络 (CNN). 在材料科学应用中,GNN提供可解释的洞察力,并且需要更少的计算能力.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 材料中的人工智能
背景情况:
- 深度学习通过预测微观结构与属性关系来加速材料科学.
- 卷积神经网络 (CNN) 可以分析3D微结构,但需要大量的资源.
- 在CNN中,对于复杂材料分析的网络规模和培训时间存在限制.
研究的目的:
- 训练和评估一个图形神经网络 (GNN),用于预测Ni-Al合金的机械性能.
- 为了比较GNN与CNN的效率和可解释性,用于微观结构分析.
- 利用可解释的人工智能来更深入地了解物质属性预测.
主要方法:
- 使用相场建模生成Ni-Al合金微结构.
- 在这些微结构上训练了一个图形神经网络 (GNN),以预测机械性质演变.
- 采用突出性分析和贝叶斯推理用于模型解释性和参数确定.
主要成果:
- 该GNN准确地预测了各种微结构大小和尺寸的合金强化.
- 与CNN相比,GNN表现出更高的性能,需要更少的GPU利用率.
- 可解释的人工智能工具为GNN的预测提供了可解释的见解.
结论:
- GNN提供了一种准确,高效和可解释的方法,用于从材料微观结构中提取信息.
- 在微观结构大小和维度限制方面,GNN克服了CNN的局限性.
- 这种方法推进了材料性质的预测,并通过可解释的AI增强了理解.
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