Speaker
Description
Abstract
Identification of materials with two-dimensional properties is crucial for the creation of next-generation technologies, yet remains a computationally intensive task. The study presents an automated pipeline designed to predict if a material has two-dimensional characteristics from its physical and structural characteristics. Two-dimensional (2D) materials are a unique category of materials that are defined by their ultra thinness, usually just one or a few layers of atoms [1]. In contrast to traditional three-dimensional materials, 2D materials are extremely thin but extend significantly in the other two directions (length and width). They are important because they exhibit exceptional strength, electrical conductivity, flexibility, and surface sensitivity due to their atomic-scale thickness [2]. These unique properties enable breakthroughs in electronics, energy storage, medicine, and environmental technologies [1]. In biomedical applications, 2D materials are used for targeted drug delivery, biosensing, tissue engineering, and bioimaging [3] [4]. Their biocompatibility, high surface area, and tunable properties make them ideal for developing next-generation medical therapies and diagnostics [6].
We extracted key features from JSON files containing material properties and CIF files describing atomic structures, both of which were obtained from the Materials Project - a publicly available database for materials science research. These features were combined to classify each material based on its dimensionality, helping identify strong 2D material candidates. Random Forest is a machine learning algorithm that is mainly used for classification and regression tasks [5]. It works by creating many decision trees during training and then combining their results to make a more accurate and stable prediction. It learns patterns from material properties and votes to classify if the material is likely 2D or not [5] [7]. The Random Forest Classifier was trained with this data to predict the material dimensionality automatically. The model performance was evaluated with feature importance analysis, confusion matrix, and classification report with high predictive capacity. The dataset included a total of 120 samples, with 96 used for training and 24 for testing across three classes (0, 1, and 2). Testing on the test set resulted in a confusion matrix of 19 predicted correctly to be class 0, 2 as class 1, and 1 out of 3 as class 2, with class-wise accuracies of 100% (class 0), 100% (class 1), and 33% (class 2). Overall, the model achieved an accuracy of 92%, highlighting its reliability for 2D material screening.
References
[1] Butler, S. Z., et al. (2013). Progress, challenges, and opportunities in two-dimensional materials beyond graphene. ACS Nano, 7(4), 2898–2926. https://doi.org/10.1021/nn400280c
[2] Ward, L., et al. (2016). A general-purpose machine learning framework for predicting properties of inorganic materials. npj Computational Materials, 2(1), 1–7. https://doi.org/10.1038/npjcompumats.2016.28
[3] Gengxin Wu, Brea B. Yang & Ying-Wei Yang(2025). Two-dimensional (2D) materials for biomedical applications. APL Materials, 13(3), 030401. https://doi.org/10.1063/5.0261156
[4] A. Murali, G. Lokhande, K. A. Deo, A. Brokesh, and A. K. Gaharwar, “Emerging 2D nanomaterials for biomedical applications,” Materials Today, vol. 50, pp. 276–302, Nov. 2021, doi: 10.1016/j.mattod.2021.04.020. https://doi.org/10.1016/j.mattod.2021.04.020
[5] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
[6] D. Chauhan, M. Ashfaq, N. Talreja, and R. V. Managalraja, “2D materials for environment, energy, and biomedical applications,” J. Biomed. Res. Environ. Sci., vol. 2, no. 10, pp. 977–984, 2021, doi: 10.37871/jbres1340.
[7] Wang, Y., Xie, Y., Li, Y., & Wang, Y. (2021). Machine learning for 2D materials: From data mining to knowledge discovery. Materials Today, 47, 93–114. https://doi.org/10.1016/j.mattod.2021.05.001
