Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques

Shetty, Vidyasagar; B, Shabari Shedthi; Kumaraswamy, J.

doi:10.1016/j.matpr.2021.09.208

Cited by 15 publications

(5 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is necessary to raise awareness at all levels of society, using various communication and educational tools, to reduce the likelihood of the health dangers spreading. Healthcare waste includes potentially hazardous bacteria that can potentially infect hospital patients, healthcare employees, and members of the public at large [32][33][34].…”

Section: Nanocoated Paper With Colour Code Marking Methodsmentioning

confidence: 99%

Improvement Study on Blood Test Investigation by Nanoparticle‐Coated Colour‐Coded Sample Paper

Jayakumar

Padmanabhan

Rani

et al. 2022

Journal of Nanomaterials

View full text Add to dashboard Cite

Nanobiotechnology is a growing branch of research that combines biology and nanotechnology to study and create unique, functionalized nanobiosystems. This interdisciplinary study subject has significant promise for improving medical engineering. Finding the exact blood category is a coercive test for blood transfer and other incalculable health checkups. The pandemic has accelerated these test frequencies in an exponential fashion. It is usually the job of the practitioners working in the laboratory to name the types of blood samples. Depending on the blood type and its cross-matching fitness, recommendations are offered for blood transfer. In many places, this type of testing is done manually, and it is done through a microplate and test tube. These tests usually take longer, and the chances of errors are high, which can lead to fatalities. Polymer-based paper sample collection has been shown to be ideal instruments for conducting blood tests in response to this requirement because of their capacity to do quick and low-cost diagnostics and analyses. In this paper, a novel nanocoated blood polymer sample paper with unique coding was proposed and investigated for its operational simplicity and effectiveness. A unique coded polymer sample paper has prevented complications during blood tests and provides expected results without errors.

show abstract

Section: Nanocoated Paper With Colour Code Marking Methodsmentioning

confidence: 99%

Improvement Study on Blood Test Investigation by Nanoparticle‐Coated Colour‐Coded Sample Paper

Jayakumar

Padmanabhan

Rani

et al. 2022

Journal of Nanomaterials

View full text Add to dashboard Cite

show abstract

“…This includes determining which elements can form perovskites and understanding the different structural and compositional variations that are possible within the perovskite structure. 131,132 Many models are successfully established in different kinds of perovskites. 133–135 Taking the electrical and geometrical factors into account, machine learning models established by Li et al were used to predict the formation of perovskite structures and showcased 96.55% and 91.83% accuracy in the single and double perovskite databases.…”

Section: Perovskitementioning

confidence: 99%

Machine learning in energy chemistry: introduction, challenges and perspectives

2023

Energy Adv.

View full text Add to dashboard Cite

show abstract

“…The findings imply that by narrowing the space of important chemical compositions, machine learning may be utilised to rapidity up extraordinary-amount DFT computations (by at minimum a factor of 5) without sacrificing accuracy 58 . Different prediction models of machine learning algorithms like NN, KNN, RF, SVM, DT and AdaBoost" classifiers are recognised to expect thermodynamic stability 59 .…”

Section: Supervised Learningmentioning

confidence: 99%

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Shetty,

Shabari Shedthi,

Shashishekar

2023

jmmf

Self Cite

View full text Add to dashboard Cite

The ultimate objective of modern engineering applications in mining and material science is to develop good quality novel materials with desirable qualities. Machine Learning (ML) is used in the mining industry to provide solutions to complex problems of the mining industry and improve the efficiency of the overall system. ML methods are increasingly being used by materials scientists to uncover hidden trends in data and generate predictions. Furthermore, data centric techniques can provide useful insights into the basic processes that influence material behaviour while simultaneously reducing human labour in large data processing. The ability of persons to find new materials and infer complex relationships is important for the development of new materials. Large amounts of machine-readable data must be available to use statistical methodologies to speed materials research. In mining engineering, ML can be used for analyzing geographical data, assessing the risk of rock fall, predicting equipment failures and impact of mining activities on the environment etc. Material science data may be used in a variety of ways, including property prediction, the search for new materials and discovering synthesis methods. Selecting proper machine learning techniques to provide solutions is very important and that is discussed here. The purposes of this paper are to provide a comprehensive list of different ML techniques which are applied for the mining and material science domain.

show abstract

Predicting the thermodynamic stability of perovskite oxides using multiple machine learning techniques

Cited by 15 publications

References 37 publications

Improvement Study on Blood Test Investigation by Nanoparticle‐Coated Colour‐Coded Sample Paper

Improvement Study on Blood Test Investigation by Nanoparticle‐Coated Colour‐Coded Sample Paper

Machine learning in energy chemistry: introduction, challenges and perspectives

Application and Challenges of Machine Learning Techniques in Mining Engineering and Material Science

Contact Info

Product

Resources

About