Wang Kewei, Gupta Vishu, Lee Claire Songhyun, Mao Yuwei, Kilic Muhammed Nur Talha, Li Youjia, Huang Zanhua, Liao Wei-Keng, Choudhary Alok, Agrawal Ankit
Electrical and Computer Engineering, Northwestern University, Evanston, 60201, USA.
Sci Rep. 2024 Oct 24;14(1):25178. doi: 10.1038/s41598-024-76535-2.
Recent progress in deep learning has significantly impacted materials science, leading to accelerated material discovery and innovation. ElemNet, a deep neural network model that predicts formation energy from elemental compositions, exemplifies the application of deep learning techniques in this field. However, the "black-box" nature of deep learning models often raises concerns about their interpretability and reliability. In this study, we propose XElemNet to explore the interpretability of ElemNet by applying a series of explainable artificial intelligence (XAI) techniques, focusing on post-hoc analysis and model transparency. The experiments with artificial binary datasets reveal ElemNet's effectiveness in predicting convex hulls of element-pair systems across periodic table groups, indicating its capability to effectively discern elemental interactions in most cases. Additionally, feature importance analysis within ElemNet highlights alignment with chemical properties of elements such as reactivity and electronegativity. XElemNet provides insights into the strengths and limitations of ElemNet and offers a potential pathway for explaining other deep learning models in materials science.
深度学习的最新进展对材料科学产生了重大影响，加速了材料的发现与创新。ElemNet是一种根据元素组成预测形成能的深度神经网络模型，它体现了深度学习技术在该领域的应用。然而，深度学习模型的“黑箱”性质常常引发人们对其可解释性和可靠性的担忧。在本研究中，我们提出了XElemNet，通过应用一系列可解释人工智能（XAI）技术来探索ElemNet的可解释性，重点是事后分析和模型透明度。对人工二元数据集的实验表明，ElemNet在预测元素周期表各基团中元素对系统的凸包方面是有效的，这表明它在大多数情况下能够有效识别元素间的相互作用。此外，ElemNet内部的特征重要性分析突出了与元素化学性质（如反应性和电负性）的一致性。XElemNet深入了解了ElemNet的优势和局限性，并为解释材料科学中的其他深度学习模型提供了一条潜在途径。
