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Chalcogenide glasses possess various outstanding properties enabling essential applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite their ubiquitous usage, these materials’ composition–property relationships remain poorly understood, impeding the pace of their discovery. Here, we use a large experimental dataset comprising ~24,000 glass compositions made of 51 distinct elements from the periodic table to develop machine learning (ML) models for predicting 12 properties, namely, annealing point, bulk modulus, density, Vickers hardness, Littleton point, Young’s modulus, shear modulus, softening point, thermal expansion coefficient, glass transition temperature, liquidus temperature, and refractive index. These models are the largest regarding the compositional space and the number of properties covered for chalcogenide glasses. Further, we use SHAP, a game theory-based algorithm, to explain the properties’ compositional control by quantifying each element's role toward model predictions. This work provides a powerful tool for interpreting the model’s prediction and designing new chalcogenide glass compositions with targeted properties. Finally, using the trained ML models, we develop several glass-selection charts that can potentially aid in the rational design of novel chalcogenide glasses for various applications.

As an alternative to liquid electrolytes, all-solid-state sodium-ion batteries are receiving significant attention due to their potential for improved safety and efficiency. Here, we propose a combined experimental and machine learning (ML) approach for discovering glass electrolytes while also providing insights into the role of different glass components. Specifically, we experimentally prepare and measure the ionic conductivity of 27 glass compositions of the sodium aluminophosphate glass family. Further, we train ML models on this dataset to predict the ionic conductivity, which exhibits excellent agreement with the experimental results. We interpret the composition–conductivity relationship learned by the ML model using Shapely additive explanations (SHAP), which reveals the role played by the glass components in governing the conductivity. Employing these observations, glass compositions with improved conductivity values are predicted and experimentally validated. The results corroborate the insights from SHAP analysis and enable optimized glass formulations in real-world experiments. This demonstrates how ML tools can significantly accelerate the discovery of Na-ion-conducting glass electrolytes.

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