Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures

Alcobaça, Edesio; Mastelini, Saulo Martiello; Botari, Tiago; Pimentel, Bruno Almeida; Cassar, Daniel R.; Carvalho, André C. P. L. F. de; Zanotto, Edgar Dutra

doi:10.1016/j.actamat.2020.01.047

Cited by 81 publications

(41 citation statements)

References 31 publications

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“…26,27 Recently, machine learning (ML) approaches have been used to develop predictive models for optical, electronic, and mechanical properties of glasses. 26,[28][29][30][31][32][33][34][35][36][37][38] Several recent works have shared composition-property databases along with the trained ML models. 28,30,36,39 For instance, the software package, Python for Glass Genomics (PyGGi), has a large composition-property database, ML models for predicting nine key properties and an optimization framework for targeted glass discovery.…”

Section: Introductionmentioning

confidence: 99%

“… 26 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 Several recent works have shared composition–property databases along with the trained ML models. 28 , 30 , 36 , 39 For instance, the software package, Python for Glass Genomics (PyGGi), has a large composition–property database, ML models for predicting nine key properties and an optimization framework for targeted glass discovery. 40 These models, however, have relied on existing databases for their training and analysis, 41 and hence have been restricted to parameter predictions through regression models.…”

Section: Introductionmentioning

confidence: 99%

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Looking through glass: Knowledge discovery from materials science literature using natural language processing

et al. 2021

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Looking through glass: Knowledge discovery from materials science literature using natural language processing Highlights d Natural language processing is used for information extraction from research papers d Caption cluster plots are used for exploring figure captions across the entire corpus d Elemental maps are used to identify the chemical elements reported in a study d A framework to extract domain-specific queries from the literature

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Looking through glass: Knowledge discovery from materials science literature using natural language processing

et al. 2021

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“…However, the accuracy of the models in that study was found to be limited by an "overlapping" region (i.e., a region of similar compositions that exhibited different crystallization behavior). 8 ML has been recently used in materials science, [10][11][12] and for predicting glass properties such as glass transition temperature, 13 density, 14 elastic modulus, 14,15 and dissolution. [16][17][18][19] However, misleading information or conclusions can be obtained when using ML methods without robust protocols.…”

Section: Introductionmentioning

confidence: 99%

Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Sargin

Vienna

2021

J. Am. Ceram. Soc.

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Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to the negative effect on chemical durability often associated with its formation. Developing models to accurately predict nepheline precipitation from compositions is important for increasing waste loading since existing models can be overly conservative. In this study, an expanded dataset of 955 glasses, including 352 high-level waste glasses, was compiled from literature data. Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. In addition, nine machine learning (ML) algorithms (k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability to predict nepheline precipitation from glass compositions. Model accuracy, precision, recall/ sensitivity, and F1 scores were systemically compared between different ML algorithms and modeling protocols. Model prediction with an accuracy of ~0.9 (misclassification rate of ~10%) was observed for different algorithms under certain protocols.This study evaluated various ML models to predict nepheline precipitation in waste glasses, highlighting the importance of data preparation and modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitation.

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“…Owing to these reasons, it is unsurprising that, just in the last ten years, numerous articles describing the use of ML models to predict properties of complex material systems (e.g., concrete; multicomponent glasses; etc.) have been published [54][55][56][57][58][59][60][61][62][63][64][65] . In our literature review, while we found scores of studies that employed ML models to predict mechanical properties of cementitious systems, we did not find any study that focused on prediction of time-dependent hydration kinetics of cement.…”

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confidence: 99%

Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems

Lapeyre

Han

Wiles

et al. 2021

Sci Rep

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Carbonaceous (e.g., limestone) and aluminosilicate (e.g., calcined clay) mineral additives are routinely used to partially replace ordinary portland cement in concrete to alleviate its energy impact and carbon footprint. These mineral additives—depending on their physicochemical characteristics—alter the hydration behavior of cement; which, in turn, affects the evolution of microstructure of concrete, as well as the development of its properties (e.g., compressive strength). Numerical, reaction-kinetics models—e.g., phase boundary nucleation-and-growth models; which are based partly on theoretically-derived kinetic mechanisms, and partly on assumptions—are unable to produce a priori prediction of hydration kinetics of cement; especially in multicomponent systems, wherein chemical interactions among cement, water, and mineral additives occur concurrently. This paper introduces a machine learning-based methodology to enable prompt and high-fidelity prediction of time-dependent hydration kinetics of cement, both in plain and multicomponent (e.g., binary; and ternary) systems, using the system’s physicochemical characteristics as inputs. Based on a database comprising hydration kinetics profiles of 235 unique systems—encompassing 7 synthetic cements and three mineral additives with disparate physicochemical attributes—a random forests (RF) model was rigorously trained to establish the underlying composition-reactivity correlations. This training was subsequently leveraged by the RF model: to predict time-dependent hydration kinetics of cement in new, multicomponent systems; and to formulate optimal mixture designs that satisfy user-imposed kinetics criteria.

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Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures

Cited by 81 publications

References 31 publications

Looking through glass: Knowledge discovery from materials science literature using natural language processing

Looking through glass: Knowledge discovery from materials science literature using natural language processing

Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Machine learning enables prompt prediction of hydration kinetics of multicomponent cementitious systems

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