Machine Learning Approach to Analyze the Surface Properties of Biological Materials

Rickert, Carolin A.; Hayta, Elif N.; Selle, Daniel M.; Kouroudis, Ioannis; Harth, Milan; Gagliardi, Alessio; Lieleg, Oliver

doi:10.1021/acsbiomaterials.1c00869

Cited by 15 publications

(12 citation statements)

References 60 publications

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“…Finally, our citation analysis revealed the most influential publications in this field, as well as the most highly cited articles [26]. We found that several articles have received many citations, indicating that they have had a significant impact on the field.…”

Section: Methodsmentioning

confidence: 70%

A Bibliometric Study of Machine Learning in Biofilm

Ding¹

2023

Preprint

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Biofilm is a complex community of microorganisms that are attached to surfaces and encased in a self-produced extracellular matrix. Machine learning (ML) techniques have been applied to various aspects of biofilm research, such as predicting biofilm formation, identifying key genes, and designing new therapeutic strategies. In this study, we conducted a bibliometric analysis of machine learning in biofilm research to provide a comprehensive overview of the current state of the field. We searched the Web of Science database for articles published included "machine learning biofilm". A total of 126 articles were identified and analysed. Our results showed that the number of publications on machine learning in biofilm has been increasing rapidly over the past decade, indicating a growing interest in the application of ML techniques to biofilm research. The analysis also revealed that the most common research topics in this area were related to biofilm formation, prediction, and control. Furthermore, the most frequently used ML techniques in biofilm research were artificial neural networks and support vector machines. Overall, our study provides valuable insights into the current trends and future directions of machine learning in biofilm research. It also highlights the importance of interdisciplinary collaboration between biofilm researchers and ML experts to drive innovation in this field.

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Section: Methodsmentioning

confidence: 70%

A Bibliometric Study of Machine Learning in Biofilm

Ding¹

2023

Preprint

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“…68 Compared with linear regression, the machine learning based on neural network performs well in large data sets. 69,70 In the case of large amount of data, it is difficult to improve the quality of linear regression model, which is caused by the increase of the characteristics of neural network model with the increase of training data. When the library entered the human controlled area, 2–6 organic synthetic chemists were conducting a joint process of molecular evaluation.…”

Section: Case Studies Of Materials Design Based On Mechine Lerningmentioning

confidence: 99%

Perspectives on development of biomedical polymer materials in artificial intelligence age

Xie

2023

J Biomater Appl

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Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs.

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“…However, sometimes the microstructures are not amenable to characterization by regular methods. Under such circumstances, machine learning methods such as convolutional neural networks (CNNs) can be used to characterize microstructure images …”

Section: Introductionmentioning

confidence: 99%

“…Under such circumstances, machine learning methods such as convolutional neural networks (CNNs) can be used to characterize microstructure images. 15 The microstructure of the crack surface is one such material system that is not trivial to characterize. In the literature, basic features of complex surfaces have been characterized using the spectral density function 16 and by extracting the h-2 value.…”

Section: Introductionmentioning

confidence: 99%

Crack Surface Analysis of Elastomers Using Transfer Learning

Ghumman

Chen

D'Angelo

et al. 2023

ACS Appl. Mater. Interfaces

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Cracks that form during fatigue offer critical information regarding the fracture process of the associated material, such as the crack speed, energy dissipation, and material stiffness. Characterization of the surfaces formed after these cracks have propagated through the material can provide important information complementary to other in-depth analyses. However, because of the complex nature of these cracks, their characterization is difficult, and most of the established characterization techniques are inadequate. Recently, Machine Learning techniques are being applied to image-based material science problems in predicting structure−property relations. Convolutional neural networks (CNNs) have proven their capacity on modeling complex and diverse images. The downside of CNNs for supervised learning is that that they require large amounts of training data. One work-around is using a pre-trained model, i.e., transfer learning (TL). However, TL models cannot be used directly without modification. In this paper, to use TL for crack surface feature−property mapping, we propose to prune the pre-trained model to retain the weights of the first several convolutional layers. Those layers are then used to extract relevant underlying features from the microstructural images. Next, principal component analysis (PCA) is used to further reduce the feature dimension. Finally, the extracted crack features together with the temperature effect are correlated with the properties of interest using regression models. The proposed approach is first tested on artificial microstructures created by spectral density function reconstruction. It is then applied to experimental data of silicone rubbers. With the experimental data, two analyses are performed: (i) analysis of the correlation of the crack surface feature and material property and (ii) predictive model for property estimation, whereby the experiments can be potentially replaced altogether.

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Machine Learning Approach to Analyze the Surface Properties of Biological Materials

Cited by 15 publications

References 60 publications

A Bibliometric Study of Machine Learning in Biofilm

A Bibliometric Study of Machine Learning in Biofilm

Perspectives on development of biomedical polymer materials in artificial intelligence age

Crack Surface Analysis of Elastomers Using Transfer Learning

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