Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States.
J Phys Chem Lett. 2021 Nov 25;12(46):11476-11487. doi: 10.1021/acs.jpclett.1c03291. Epub 2021 Nov 18.
Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.
理解化学键的本质及其在物理可调因素下强度的变化对于新型催化材料的开发非常重要。一种加快这一进程的方法是使用机器学习（ML）算法，并利用来自高通量实验或量子化学模拟的在线数据存储库进行训练。尽管 ML 模型在预测固体表面反应性方面具有合理的预测性能，但现代算法（例如深度学习）的日益复杂性使得它们成为几乎没有解释的黑盒。在本观点中，我们从特征工程、算法开发和事后分析的角度讨论了可解释 ML 的最新进展，以打开这些黑盒。我们强调了可解释性作为下一代 ML 算法和新兴 AI 平台的基础的关键作用，这些平台可以推动各个科学领域的发现。
