Materials Design in Digital Era: Challenges and Opportunities

Jain, Deepak; Dwadasi, Balarama Sridhar; Kumar, Dharmendr; Mishra, Shashank Shekhar; Ravikumar, Bharath; Gupta, Rakesh; Srinivasan, Sriram Goverapet; Jain, Vinay; Mynam, Mahesh; Maiti, Soumyadipta; Rai, Beena

doi:10.1007/s12666-019-01702-3

Cited by 2 publications

(3 citation statements)

References 78 publications

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“…In the following section the application of machine learning and deep learning in corrosion inhibition research are presented [21][22][23][24][25][26][27][28][29][30][31].…”

Section: Machine Learning and Corrosion Inhibition Studymentioning

confidence: 99%

“…Current advances in computational capabilities, combined with improved quantum mechanical algorithms, have paved the way for accelerated material design and screening. Jain et al [27] described how they use modelling and simulation to design/screen materials for a variety of applications including transdermal drug delivery, high-strength alloys, lithium-ion batteries, corrosion inhibition, mineral processing, and rareearth element recovery. Each of the preceding examples employs high-performance computingbased first-principle simulations (ranging from the electronic to molecular scales) to arrive at a promising material, generating massive amounts of 'data' in the process.…”

Section: Materials Design In Digital Eramentioning

confidence: 99%

“…Machine Learning is a broad field that encompasses a wide range of machine learning operations such as clustering, classification, and the development of predictive models [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Machine learning is also being used in corrosion science and engineering [21][22][23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning in corrosion inhibition study

Dorothy¹,

Sasilatha²,

Rajendran³

et al. 2022

Zaštita materijala

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Artificial intelligence is a branch of science concerned with teaching machines to think and act like humans. Machine learning is concerned with enabling computers to perform tasks without the need for explicit programming. Machine Learning enables computers to learn without the need for explicit programming. Machine Learning is a broad field that encompasses a wide range of machine learning operations such as clustering, classification, and the development of predictive models. Machine Learning (ML) and Deep Learning (DL) research is now finding a home in both industry and academia. Machine Learning technologies are increasingly being used in medical imaging. To detect tumours and other malignant growths in the human body. Deep Learning is making significant contributions to the advancement of industrial robotics. Machine learning algorithms are used in the self-driving car industry to guide the vehicle to its destination. Deep Learning and Machine Learning are also used in corrosion science and engineering. They are used to choose the inhibitor molecules from a large pool of available molecules.

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“…In the following section the application of machine learning and deep learning in corrosion inhibition research are presented [21][22][23][24][25][26][27][28][29][30][31].…”

Section: Machine Learning and Corrosion Inhibition Studymentioning

confidence: 99%

Section: Materials Design In Digital Eramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of machine learning in corrosion inhibition study

Dorothy¹,

Sasilatha²,

Rajendran³

et al. 2022

Zaštita materijala

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Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence

Xu,

Gao,

Wang

et al. 2024

ACS Appl. Mater. Interfaces

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This study proposes an innovative paradigm for metaverse-based synthesis experiments, aiming to enhance experimental optimization efficiency through human-guided parameter tuning in the metaverse and augmented artificial intelligence (AI) with human expertise. By integration of the metaverse experimental system with automated synthesis techniques, our goal is to profoundly extend the efficiency and advancement of materials chemistry. Leveraging advanced software algorithms and simulation techniques within the metaverse, we dynamically adjust synthesis parameters in real time, thereby minimizing the conventional trial-and-error methods inherent in laboratory experiments. In comparison fully AI-driven adjustments, this human-intervened approach to metaverse parameter tuning achieves desired results more rapidly. Coupled with automated synthesis techniques, experiments in the metaverse system can be swiftly realized. We validate the high synthesis efficiency and precision of this system through NaYF4:Yb/Tm nanocrystal synthesis experiments, highlighting its immense potential in nanomaterial studies. This pioneering approach not only simplifies the process of nanocrystal preparation but also paves the way for novel methodologies, laying the foundation for future breakthroughs in materials science and nanotechnology.

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Materials Design in Digital Era: Challenges and Opportunities

Cited by 2 publications

References 78 publications

Application of machine learning in corrosion inhibition study

Application of machine learning in corrosion inhibition study

Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence

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