# An Interpretable XGBoost Framework for Predicting Oxide Glass Density
## 1 Introduction
In recent years, materials science has been undergoing a paradigm shift driven by the availability of large-scale computational power and extensive materials databases. The rise of data-driven methods, especially machine learning (ML), powered by comprehensive databases like SciGlass [9] or INTERGLAD [10], which contains data on hundreds of thousands of glasses, has created new opportunities for materials modeling and discovery. This approach allows for the development of predictive models with a much broader scope than their classical counterparts. Previous studies have consistently demonstrated that ML models, utilizing inputs such as glass chemical composition (in molar percentages of oxides/components) and ionic packing ratio, can accurately predict density across various glass chemistries including oxide [11; 12; 13; 14], fluoride [15], and multicomponent systems [11; 16]. Algorithms such as Random Forest (RF) regression, Artificial Neural Networks (ANN)/Multilayer Perceptron(MLP), and Gaussian Process Regression (GPR) have shown very good performance, frequently outperforming conventional empirical models. For instance, RF and ANN models have consistently achieved high R\({}^{2}\) values, often in the range of c.a. 0.95-0.97 for oxide glasses with mean square error (MSE) of about 0.03-0.04 g/cm\({}^{3}\) and mean absolute percentage error (MAPE) of c.a. 0.63-0.83%, significantly surpassing the R\({}^{2}\) values (e.g., c.a. 0.28-0.91) and MSE (e.g., c.a. 0.05-0.15 g/cm\({}^{3}\)) of empirical methods [11, 12, 16]. For fluoride glasses, RF achieved an R\({}^{2}\) of 0.980, and ANNs reached 0.870 (or 0.792 for boron-rich glasses) [14, 15]. However, the efficacy of ML models in materials science is dependent on the numerical representation of the material's chemical composition--a process known as feature engineering. A simple vector of molar fractions is often insufficient. Foundational publications in the field by [PERSON] et al. and [PERSON] et al. [17] have demonstrated that model performance is significantly enhanced when the feature vector is enriched with descriptors that encode the fundamental physicochemical properties of the constituent atoms, such as atomic mass, electronegativity, and electronic structure. The development of open-source toolkits like Matminer [18] has facilitated the implementation of these advanced featurization strategies. Despite the growing success of ML, a common criticism is that these models often function as \"black boxes,\" providing accurate predictions without revealing the underlying physical mechanisms. This creates a divergence from the traditional, mechanism-driven approach in the physical sciences. The primary objective of this work is therefore not only to develop an accurate ML model for predicting the density of oxide glasses but, more importantly, to investigate how different feature engineering strategies impact model performance and interpretability. This study compares the performance of three distinct ML algorithms fed with four feature sets of increasing physicochemical complexity. The central hypothesis is that employing physically informed descriptors will enable the model to not only achieve high accuracy but also to \"learn\" the fundamental physical principles governing the material's density. The principal conclusions of this work confirm that an XGBoost (eXtreme Gradient Boosting) model, when combined with advanced Magpie features [19], achieves very good predictive accuracy. Crucially, interpretability analysis reveals that the model approximates the fundamental density equation by relying on attributes that serve as representations for molar mass and molar volume. This validates the use of properly designed ML models as powerful and physically interpretable tools for materials discovery.