Surface-Related Features Responsible for Cytotoxic Behavior of MXenes Layered Materials Predicted with Machine Learning Approach

Abstract

To speed up the implementation of the two-dimensional materials in the development of potential biomedical applications, the toxicological aspects toward human health need to be addressed. Due to time-consuming and expensive analysis, only part of the continuously expanding family of 2D materials can be tested in vitro. The machine learning methods can be used-by extracting new insights from available biological data sets, and provide further guidance for experimental studies. This study identifies the most relevant highly surface-specific features that might be responsible for cytotoxic behavior of 2D materials, especially MXenes. In particular, two factors, namely, the presence of transition metal oxides and lithium atoms on the surface, are identified as cytotoxicity-generating features. The developed machine learning model succeeds in predicting toxicity for other 2D MXenes, previously not tested in vitro, and hence, is able to complement the existing knowledge coming from in vitro studies. Thus, we claim that it might be one of the solutions for reducing the number of toxicological studies needed, and allows for minimizing failures in future biological applications.

Keywords: MXenes; cytotoxicity; machine learning; van der Waals layered materials.

Figure 1

Feature engineering for dataset I obtained for two methods: (A) Random Forest (RF) and (B) Principle Component Analysis (PCA). Both methods are methodological tools that allow simplification of the model.

Figure 2

Ranking of feature importance obtained from RF. The most important are two descriptors: (i) the presence of M_xO_y, and (ii) Li on the surface, whereas the next four: surface modification, delaminating agent, lateral size, and Cl on the surface are equally important with smaller weights than the previous two. All feature labels are described in Table A2.

Figure 3

Feature engineering for dataset III obtained for two methods: (A) Random Forest (RF) and (B) Principle Component Analysis (PCA). Both methods are methodological tools that allow simplification of the model.

Figure 4

Ranking of feature importance for dataset III. The most important are the first three descriptors, namely M_xO_y, Li on the surface and surface modifications, respectively. All feature labels are described in Table A2 and Table A3.