标题： 物理科学中从数据驱动和领域驱动视角产生的多种解释 显示英文标题

标题： Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences

摘要： 机器学习方法在材料科学中取得了显著的成功，提供了新的科学见解，指导未来的实验室实验，并加速了材料的发现。 尽管这些模型表现出色，但理解它们所做的决策对于确保其结果的科学价值同样至关重要。 然而，关于解释多样性的近期和持续争论可能会导致科学上的不一致。 本文探讨了在物理科学中的机器学习应用中，这些多样化解释的来源和影响。 通过材料科学和分子性质预测的三个案例研究，我们考察了不同模型、解释方法、特征归因层次以及利益相关者需求如何导致对机器学习输出的不同解释。 我们的分析强调了在科学背景下解释机器学习模型时考虑多种观点的重要性，并突出了科学家在解释过程中保持控制的关键需求，平衡数据驱动的见解与领域专业知识以满足特定的科学需求。 通过促进对这些不一致性的全面理解，我们旨在为可解释人工智能（XAI）在物理科学中的负责任整合做出贡献，并提高机器学习在科学发现中的可信度。 显示英文摘要

摘要： Machine learning methods have been remarkably successful in material science, providing novel scientific insights, guiding future laboratory experiments, and accelerating materials discovery. Despite the promising performance of these models, understanding the decisions they make is also essential to ensure the scientific value of their outcomes. However, there is a recent and ongoing debate about the diversity of explanations, which potentially leads to scientific inconsistency. This Perspective explores the sources and implications of these diverse explanations in ML applications for physical sciences. Through three case studies in materials science and molecular property prediction, we examine how different models, explanation methods, levels of feature attribution, and stakeholder needs can result in varying interpretations of ML outputs. Our analysis underscores the importance of considering multiple perspectives when interpreting ML models in scientific contexts and highlights the critical need for scientists to maintain control over the interpretation process, balancing data-driven insights with domain expertise to meet specific scientific needs. By fostering a comprehensive understanding of these inconsistencies, we aim to contribute to the responsible integration of eXplainable Artificial Intelligence (XAI) into physical sciences and improve the trustworthiness of ML applications in scientific discovery.