Machine learning has become a common and powerful tool in materials research. As more data become available, with the use of high-performance computing and high-throughput experimentation, machine learning has proven potential to accelerate scientific research and technology development. Though the uptake of data-driven approaches for materials science is at an exciting, early stage, to realize the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust in model predictions, and unveiling unexpected correlations that may lead to scientific insights. In this work, we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies. We start by defining the fundamental concepts of interpretability and explainability in machine learning and making them less abstract by providing examples in the field. We show how interpretability in scientific machine learning has additional constraints compared to general applications. Building upon formal definitions in machine learning, we formulate the basic trade-offs among the explainability, completeness, and scientific validity of model explanations in scientific problems. In the context of these trade-offs, we discuss how interpretable models can be constructed, what insights they provide, and what drawbacks they have. We present numerous examples of the application of interpretable machine learning in a variety of experimental and simulation studies, encompassing first-principles calculations, physicochemical characterization, materials development, and integration into complex systems. We discuss the varied impacts and uses of interpretabiltiy in these cases according to the nature and constraints of the scientific study of interest. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. In particular, we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need for uncertainty estimates for model explanations. Finally, we showcase a number of exciting developments in other fields that could benefit interpretability in material science problems. Adding interpretability to a machine learning model often requires no more technical know-how than building the model itself. By providing concrete examples of studies (many with associated open source code and data), we hope that this Account will encourage all practitioners of machine learning in materials science to look deeper into their models.

中文翻译：

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材料科学和化学的可解释和可解释机器学习

机器学习已成为材料研究中常用且强大的工具。随着越来越多的数据可用，通过使用高性能计算和高通量实验，机器学习已被证明具有加速科学研究和技术发展的潜力。尽管材料科学采用数据驱动的方法处于令人兴奋的早期阶段，但要实现机器学习模型在成功科学发现方面的真正潜力，它们必须具备超越纯粹预测能力的品质。模型的预测和内部工作应该为人类专家提供一定程度的可解释性，允许识别潜在的模型问题或限制，建立对模型预测的信任，并揭示可能导致科学见解的意外相关性。在这项工作中，我们总结了可解释性和可解释性技术在材料科学和化学中的应用，并讨论了这些技术如何改善科学研究的结果。我们首先定义机器学习中可解释性和可解释性的基本概念，并通过提供该领域的示例使它们不那么抽象。我们展示了与一般应用程序相比，科学机器学习中的可解释性如何具有额外的限制。基于机器学习中的正式定义，我们制定了科学问题中模型解释的可解释性、完整性和科学有效性之间的基本权衡。在这些权衡的背景下，我们讨论了如何构建可解释的模型，它们提供了哪些见解，以及它们有哪些缺点。我们展示了许多可解释机器学习在各种实验和模拟研究中的应用示例，包括第一性原理计算、物理化学表征、材料开发以及与复杂系统的集成。我们根据感兴趣的科学研究的性质和限制，讨论这些案例中可解释性的不同影响和用途。我们讨论了材料科学中可解释机器学习的各种挑战，更广泛地说，在科学环境中。特别是，我们强调通过纯粹解释机器学习模型来推断因果关系或达到泛化的风险，以及模型解释需要不确定性估计。最后，我们展示了其他领域的许多令人兴奋的发展，这些发展可能有助于材料科学问题的可解释性。为机器学习模型添加可解释性通常不需要比构建模型本身更多的技术知识。通过提供具体的研究示例（许多具有相关的开源代码和数据），我们希望该帐户将鼓励所有材料科学机器学习从业者更深入地研究他们的模型。