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.

翻译：机器学习方法在材料科学领域取得了显著成功，为科学研究提供了新颖的见解、指导了未来的实验室实验，并加速了材料发现进程。尽管这些模型展现出令人瞩目的性能，理解其决策机制对于确保其成果的科学价值同样至关重要。然而，当前关于解释多样性的持续讨论可能引发科学解释的不一致性问题。本视角论文探讨了机器学习在物理科学应用中产生多样化解释的根源及其影响。通过材料科学和分子性质预测领域的三个案例研究，我们分析了不同模型、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差异化解读。我们的分析强调在科学语境中解释机器学习模型时考虑多元视角的重要性，并指出科学家必须在解释过程中保持主导权，平衡数据驱动见解与领域专业知识，以满足特定的科学需求。通过深化对这些不一致性的全面理解，我们旨在促进可解释人工智能在物理科学中的负责任整合，并提升机器学习在科学发现应用中的可信度。