Natural language processing-guided meta-analysis and structure factor database extraction from glass literature

Zaki, Mohd; Namireddy, Sahith; Pittie, Tanu; Bihani, Vaibhav; Keshri, Shweta R.; Venugopal, Vineeth; Gosvami, Nitya Nand; Jayadeva,; Krishnan, N. M. Anoop

doi:10.1016/j.nocx.2022.100103

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Recently, there have been several attempts to exploit the advances in machine learning (ML) and articial intelligence (AI) towards automated information extraction (IE) from the literature. [1][2][3][4] These include the development of materials specic language models, [5][6][7][8] rule-based systems, [9][10][11][12][13] IE from tables, 8,14,15 and IE from images. [16][17][18][19] The widely varying information expression styles in research papers make the automated MatSci IE a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing the materials tetrahedron: challenges in materials information extraction

Hira,

Zaki,

Sheth

et al. 2024

Digital Discovery

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Section: Introductionmentioning

confidence: 99%

Reconstructing the materials tetrahedron: challenges in materials information extraction

Hira,

Zaki,

Sheth

et al. 2024

Digital Discovery

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“…Recently, researchers have been actively using machine learning (ML) algorithms to develop composition-dependent property models with the available curated databases for predicting properties such as Young's modulus, glass transition temperature, and refractive index of oxide glasses [10][11][12][13][14][15][16][17][18][19][20][21]. These approaches, combined with artificial intelligence-based information extraction from the literature, have proven to significantly accelerate materials design and discovery by enabling the automated development of databases and knowledge bases from the literature [19,20,[22][23][24]. In addition, data-driven modeling has proved to be quite helpful in understanding the composition-property relationships, thereby enhancing the design of new glasses [25,26].…”

Section: Introductionmentioning

confidence: 99%

Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships

Singla¹,

Mannan²,

Zaki³

et al. 2023

J. Phys. Mater.

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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.

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“…The main advantage of ML is that it allows the development of a surrogate model for composition–property relationships from data, which, in turn, can be used for screening materials . Since ML approaches are data-hungry, most works rely on data obtained from the literature or produced synthetically using computer simulations. ,,,− However, glass properties are highly sensitive to the preparation and testing conditions. , Thus, the raw materials, experimental conditions, and glass preparation techniques of all the glasses must be consistent for developing accurate ML models. If available, some of these properties can be extracted from the literature employing approaches such as natural language processing. ,,, While this issue could be alleviated to a great extent by using synthetic data generated using molecular simulations, these methods have inherent limitationssuch as high cooling rates and small system sizeswhich makes the extrapolation to new glass compositions questionable.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Assisted Design of Na-Ion-Conducting Glasses

et al. 2023

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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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Natural language processing-guided meta-analysis and structure factor database extraction from glass literature

Cited by 6 publications

References 36 publications

Reconstructing the materials tetrahedron: challenges in materials information extraction

Reconstructing the materials tetrahedron: challenges in materials information extraction

Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships

Machine Learning-Assisted Design of Na-Ion-Conducting Glasses

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