Designing optical glasses by machine learning coupled with a genetic algorithm

Cassar, Daniel R.; Santos, Gisele G.; Zanotto, Edgar Dutra

doi:10.1016/j.ceramint.2020.12.167

Cited by 49 publications

(29 citation statements)

References 28 publications

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“…It should be noted obtaining the global minima is challenging in many cases, and thus, the optimization algorithms provide a family of glass compositions. A similar approach has been used in recent work to design optical glasses using genetic algorithm with ML models for refractive index ( n d ) and glass‐transition temperature ( T g ) as surrogates 25 . In this work, two criteria, namely n d >1.7 and T g <500ºC, was applied to discover glasses.…”

Section: Grand Challenges In Glass Science Engineering and Technologymentioning

confidence: 99%

“…Among these, some of the specific applications include the prediction of Young's modulus, 12‐14 liquidus temperature, 15 solubility, 16 glass‐transition temperature, 17,18 dissolution kinetics, 19,20 viscosity 8,21 . Other recent works have developed composition–property models for several important thermal, optical, and mechanical properties of glasses based on the available experimental dataset 22‐25 . A recent work has bundled these models, 22,23 along with a database and an optimization module, in a first‐of‐its‐kind software package for accelerating glass discovery, namely Python for Glass Genomics (PyGGi, see http://pyggi.iitd.ac.in).…”

Section: Introductionmentioning

confidence: 99%

“…It may be observed that most of the ML‐related work in the glass literature focus mainly on property prediction and a few on compositional optimization using ML models as surrogates 25 . However, there are multitudes of opportunities in the area of AI and ML that can accelerate the field of glass science.…”

Section: Introductionmentioning

confidence: 99%

“…8,21 Other recent works have developed composition-property models for several important thermal, optical, and mechanical properties of glasses based on the available experimental dataset. [22][23][24][25] A recent work has bundled these models, 22,23 along with a database and an optimization module, in a first-of-its-kind software package for accelerating glass discovery, namely Python for Glass Genomics (PyGGi, see http://pyggi.iitd.ac.in). 23 PyGGi is a copyrighted softwareas-a-service (SaaS) package, which is available both online and offline for use.…”

mentioning

confidence: 99%

“…It may be observed that most of the ML-related work in the glass literature focus mainly on property prediction and a few on compositional optimization using ML models as surrogates. 25 However, there are multitudes of opportunities in the area of AI and ML that can accelerate the field of glass science. In this article, we try to identify the main challenges that need to be addressed to accelerate innovation and development in the field of glass science and technology.…”

mentioning

confidence: 99%

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Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century

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The progress of human civilization has always been closely associated with the discovery of new materials. This is probably why the tripartite classification of historical periods is also based on materials-stone, bronze, and iron age.Beyond these materials, there are several others which have significantly improved the quality of human life, namely, steel, aluminum, glass, plastics, the latest in the list being nanomaterials. Among these materials, glasses hold a unique place in human lives, considering their applications ranging from everyday glass utensils and kitchen-wares to

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Section: Grand Challenges In Glass Science Engineering and Technologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century

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Prediction of Phase Composition and Process Resilience in Plasma‐Assisted Hetero‐Aggregate Synthesis using a Machine‐Learning Model with Multivariate Output

Lu,

Fazletdinov,

Pan

et al. 2024

Adv Materials Technologies

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The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade‐off relations, for example, in terms of product composition, yield, or energy efficiency. Machine‐supported process evaluation and learning (ML) of these relations enables optimization strategies for advanced material processing. Such a workflow is demonstrated on the example of plasma‐assisted aerosol deposition (PAAD) of alumina powders. Depending on processing conditions, these powders comprise of hetero‐aggregate mixtures of crystalline and amorphous polymorphs. Process optimization toward a specific target composition calls for ML approaches. For this, a sufficiently large and consistent dataset of PAAD input (processing) and output (product) parameters is initially generated by real‐world processing, and subsequently extrapolated into a cloud of ≈106 input‐output parameter matrices using Gaussian process regression with multivariate output and input‐output feature analysis. It is subsequently demonstrated how not only the phase composition of the obtained alumina powders, but also product resilience to variations in specific processing parameters, or – as a perspective – the energy efficiency of material processing can be predicted.

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Design of polymers for energy storage capacitors using machine learning and evolutionary algorithms

et al. 2021

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To meet the demands of emerging electrification technologies, polymers that are capable of withstanding high electric fields at high temperatures are needed. Given the staggeringly large search space of polymers, traditional, intuition-and experience-based Edisonian approaches are too slow at discovering new polymers that can meet these demands. In this work, a genetic algorithm was combined with five machine learning-based property predictors to design over 50,000 hypothetical polymers that achieve target properties. Additionally, a polymer synthesizabilitybased criterion was used to narrow these polymers down to 23 candidates likely to be synthesizable and 3665 that may be synthesizable. A version of the genetic algorithm code is also made available for public use on GitHub.

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Designing optical glasses by machine learning coupled with a genetic algorithm

Cited by 49 publications

References 28 publications

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century

Prediction of Phase Composition and Process Resilience in Plasma‐Assisted Hetero‐Aggregate Synthesis using a Machine‐Learning Model with Multivariate Output

Design of polymers for energy storage capacitors using machine learning and evolutionary algorithms

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Designing optical glasses by machine learning coupled with a genetic algorithm

Cited by 49 publications

References 28 publications

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century

Prediction of Phase Composition and Process Resilience in Plasma‐Assisted Hetero‐Aggregate Synthesis using a Machine‐Learning Model with Multivariate Output

Design of polymers for energy storage capacitors using machine learning and evolutionary algorithms

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Product

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Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21^st century