Density of fluoride glasses through artificial intelligence techniques

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

“…In this investigation, we employed three types of ML algorithms to predict the room-temperature densities of CMASTFNKM glasses as a function of their chemical compositions, namely, ANN, RF, and GPR, all of which have been used in the glass community to predict glass properties. 6,8,[20][21][22][23] ANN modeling works by propagating raw information from an input layer (i.e., chemical compositions of glasses here) through the hidden neurons in between (where the raw information is processed) all the way to the final output layer to generate a prediction (i.e., glass density). Each hidden neuron can be mathematically described using the following equation:…”

“…21 More recently, Ahmmad et al also developed ANN and RF models to predict glass density for oxide 6 and fluoride glasses. 22 Another powerful ML technique, that is, Gaussian process regression (GPR), has recently been used to predict glass density along with a range of other properties (e.g., Young's modulus, hardness, and thermal expansion coefficient) for a wide range of oxide glasses. 23 The density data used to train ML models in the aforementioned studies is mostly experimentally determined, whereas a recent study 24 employed high-throughput atomistic simulations to estimate the density and elastic moduli of silicate-based glasses, which were then used to train the ML model (i.e., least absolute shrinkage and selection operator with a gradient boost machine).…”

“…In addition to density, ML techniques have also been increasingly used to predict other glass properties, including elastic properties, glass transition temperature, coefficient of thermal expansion, liquidus temperature, refractive index, and chemical durability. [25][26][27][28] Synthesizing across the aforementioned studies 5,7,10,14,15,17,18,[20][21][22][23][24]27 reveals that there is still a lack of ML studies focusing on density prediction for silicate-based glasses that are relevant to cement and concrete applications. For the production of concrete, industrial waste rich in glassy amorphous phases (e.g., blast-furnace slags from steel production, fly ashes from coal-fired power plants, and waste glasses) or naturally derived materials (e.g., volcanic ashes and calcined clays) can be used to partially replace Portland cement (PC) [29][30][31] to lower the CO 2 emissions associated with the use of PC (the dominant cement product in the current market) in concrete.…”

“…More recently, Ahmmad et al. also developed ANN and RF models to predict glass density for oxide 6 and fluoride glasses 22 . Another powerful ML technique, that is, Gaussian process regression (GPR), has recently been used to predict glass density along with a range of other properties (e.g., Young's modulus, hardness, and thermal expansion coefficient) for a wide range of oxide glasses 23 .…”