Machine Learning Approach to Analyze the Surface Properties of Biological Materials,ACS Biomaterials Science & Engineering

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Machine Learning Approach to Analyze the Surface Properties of Biological Materials
ACS Biomaterials Science & Engineering ( IF 5.4 ) Pub Date : 2021-08-20 , DOI: 10.1021/acsbiomaterials.1c00869
Carolin A Rickert _{1,

2} , Elif N Hayta _{1,

2} , Daniel M Selle _{1,

2} , Ioannis Kouroudis ₃ , Milan Harth ₃ , Alessio Gagliardi ₃ , Oliver Lieleg _{1,

2}

Affiliation

Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fast-evolving method that allows for analyzing big data and unveiling correlations that otherwise would remain undiscovered. It may hold invaluable potential to engineer novel functional materials with desired properties, a field, which is currently limited by time-consuming trial and error approaches and our limited understanding of how different material properties depend on each other. Here, we apply machine learning algorithms to classify complex biological materials based on their microtopography. With this approach, the surfaces of different variants of biofilms and plant leaves can not only be distinguished but also correctly classified according to their wettability. Furthermore, an importance ranking provided by one of the algorithms allows us to identify those surface features that are critical for a successful sample classification. Our study exemplifies how machine learning can contribute to the analysis and categorization of complex surfaces, a tool, which can be highly useful for other areas of materials science, such as damage assessment as well as adhesion or friction studies.

中文翻译：

分析生物材料表面特性的机器学习方法

类似于 CRISPR 如何彻底改变分子生物学领域，机器学习可能会极大地促进材料科学领域的研究。机器学习是一种快速发展的方法，可以分析大数据并揭示否则将不被发现的相关性。设计具有所需特性的新型功能材料可能具有宝贵的潜力，该领域目前受到耗时的试错方法以及我们对不同材料特性如何相互依赖的有限理解的限制。在这里，我们应用机器学习算法根据其微地形对复杂的生物材料进行分类。通过这种方法，不仅可以区分生物膜和植物叶子的不同变体的表面，还可以根据其润湿性正确分类。此外，其中一种算法提供的重要性排名使我们能够识别那些对成功样本分类至关重要的表面特征。我们的研究举例说明了机器学习如何有助于复杂表面的分析和分类，这是一种对材料科学的其他领域非常有用的工具，例如损伤评估以及粘附或摩擦研究。

更新日期：2021-09-13

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