Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal-organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25-30% decrease in root-mean-squared-deviation and an average increase of 40-50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure--property relationships for materials design.
翻译:机器学习已成为一种强大的材料发现方法。 它的主要挑战在于选择能够创造材料可解释的描述的功能, 并且能够跨越多种预测任务。 我们引入了一个端到端机器学习模型, 自动生成描述器, 能够捕捉材料结构和化学的复杂描述器。 这种方法建立在计算表层技术( 持久性同质学) 和自然语言处理中的单词嵌入上。 它自动包含材料系统直接提供的几何和化学信息。 我们通过预测不同条件下的甲烷和二氧化碳吸附, 展示了我们对多种纳米金属- 有机框架数据集的处理方法。 我们的结果显示,与从常用的手工加工特征中构建的模型相比,目标的准确性和可转移性都有很大改进, 始终平均减少了25-30%的根位分布法和R2分中平均增加40- 50%。 我们方法的主要优点是可解释性: 我们的模式确定了与不同压力下吸附的最佳吸附点, 这有助于理解材料设计的原子层次结构- 和压力关系。