Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods

Zhai, Xiao; Chen, Mingtong; Lu, Wei

doi:10.1016/j.commatsci.2018.04.031

Cited by 56 publications

(17 citation statements)

References 53 publications

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“…In 2018, Zhai et al 75 developed a prediction model of Tc with physicochemical parameters based on ML. In the meanwhile, the potential perovskite material (La 0.66 Sr 0.3 Ba 0.04 MnO 3 ) with high Tc of 390.35°C were found from the virtual samples by the SVR model combined with the genetic algorithm search strategy.…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

Machine learning for perovskite materials design and discovery

et al. 2021

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The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

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Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

Machine learning for perovskite materials design and discovery

et al. 2021

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“…The calculations were performed on Online Computational Platform of Material Data Mining 37,38) developed by us. It can be freely used on the website (http://matdata.shu.…”

Section: Implementmentioning

confidence: 99%

Fuel Ratio Optimization of Blast Furnace Based on Data Mining

Zhai

Chen

2020

ISIJ Int.

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Despite the age of the process, the blast furnace (BF) ironmaking is still crucial to iron and steel industry. To improve the competitiveness of enterprises, fuel ratio (FR) in BF ironmaking process needs to be kept its lowest level possibly. In the work, a prediction model was established to predict FR of the BF by using feature selection and support vector regression (SVR). GA-SVR (genetic algorithm-SVR) method was employed to select the most informative five features from the candidate features.The experimental results indicated that the SVR model brought high learning precision and excellent prediction generalization ability. To explore and discover the laws of BF production, the influences of the five features on FR were discussed by simulation analysis of the model. All the calculations were performed on the computational platform of data mining developed by us. The work can provide guides for the operators on modulating input parameters in advance. The methods outlined here can provide valuable hints into revealing mechanisms of BF ironmaking process and realizing controlled production of BF with guidance of quantitative analysis methods.

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“…The Institute was the birth place of the famous 5-steps rule [278], which has been used in nearly all the areas of computational biology [203, 204, 215, 224-230, 233, 251, 254-256, 259-261, 264, 265, 283, 285, 294, 340, 341, 343-382]), material science [383], and even the commercial science (e.g., the bank account systems). The only difference between them is how to formulate the statistical samples or events with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted.…”

Section: Five-steps Rulementioning

confidence: 99%

Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development

Chou¹

2020

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Gordon Life Science Institute is the first Internet Research Institute ever established in the world. It is a non-profit institute. Those scientists who really dedicate themselves to science and loving science more than anything else can become its member. In the friendly door-opened Institute, they can maximize their time and energy to engage in their scientific creativity. They have also believed that science would be more truthful and wonderful if scientists do not have to spend a lot of time on funding application, and that great scientific findings and creations in history were often made by those who were least supported or funded but driven by interesting imagination and curiosity. Recollected in this review article is its establishing and developing processes, as well as its philosophy and accomplishments. Particularly, its productive and by-productive outcomes have covered the following five very hot topics in bioinformatics and drug development: 1) PseAAC and PseKNC; 2) Disported key theory; 3) Wenxiang diagram; 4) Multi-label system prediction; 5) 5-steps rule. Their impacts on the proteomics and genomics as well as drug development are substantially and awesome.

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Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods

Cited by 56 publications

References 53 publications

Machine learning for perovskite materials design and discovery

Machine learning for perovskite materials design and discovery

Fuel Ratio Optimization of Blast Furnace Based on Data Mining

Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Development

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