PSO-SVR predicting for the Ehull of ABO3-type compounds to screen the thermodynamic stable perovskite candidates based on multi-scale descriptors

Chen, Lanping; Wang, Xuechen; Xia, Wenjie; Liu, Changhai

doi:10.1016/j.commatsci.2022.111435

Cited by 13 publications

(5 citation statements)

References 25 publications

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“…Similar to the traditional particle swarm algorithm, IPSO also optimises the search based on the position and velocity of particles. However, IPSO adopts a nonlinear weighting approach in updating the position and velocity of particles and introduces an adaptive learning factor to adjust the search direction and step size, thus making the algorithm more flexible and efficient [8].…”

Section: Nonlinear Weighted Particle Swarm Algorithmmentioning

confidence: 99%

Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series

Wang,

Dong,

et al. 2024

AEMPS

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The price of gold, as an important precious metal, is highly volatile and uncertain as it is affected by the economic and political situation in the global market. Therefore, forecasting gold price is of great significance for investors, policy makers and economists. In this paper, an algorithm based on nonlinear weight decreasing PSO-SVR univariate time series prediction is proposed for forecasting gold price. The algorithm can help investors, policy makers and firms to understand market trends and price fluctuations and make more informed decisions. The algorithm is based on a nonlinear weighted particle swarm (IPSO) optimised support vector machine (SVM) time series model, which is trained with training set data and validated using test set data. Y-X scatter plots are plotted for the predicted and real values of the training set, and line plots of the predicted and real values of the training set are plotted in the coordinate system, and the results show that the algorithm is able to predict the price of the gold stock well, and the predicted and real values of the price of the gold stock can be very close to each other, both in the training set and the test set. The values of the model evaluation indexes R2, MAE, MBE and MAPE show that the algorithm can predict the gold stock price very well, and the prediction results in the test set do not deviate much from the training set. Therefore, the algorithm can provide useful market information and decision support for investors, policy makers and enterprises.

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Section: Nonlinear Weighted Particle Swarm Algorithmmentioning

confidence: 99%

Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series

Wang,

Dong,

et al. 2024

AEMPS

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“…The performance of the XGBR-144 model is comparable to or better than previously reported models for E h regression. 22,33,34,56 One of the reasons may be attributed to the adoption of the stability label as one of the features for regression model training.…”

Section: Perovskite E H Value Regressionmentioning

confidence: 99%

“…The top-performing extra trees classification algorithm achieved an accuracy of around 0.93, whereas the leading kernel ridge regression model had a RMSE of approximately 28.5 meV atom −1 . Chen et al 33 developed a particle swarm optimization-support machine regression (PSO-SVR) model to predict the E h values of ABO 3 -type compounds. The R 2 and RMSE of the model were 0.957 and 87 meV atom −1 , respectively.…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning-assisted screening of perovskite oxides

Zhao,

Wang,

et al. 2024

RSC Adv.

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“…In 2021, Zhang et al 175 identified tolerance factors from 339 initial features by SHAP (Shapley additive explanations) as one of the most influential factors of the formation of hybrid organic–inorganic perovskites, the range of which was theoretically narrowed to 0.84–1.12. In 2022, on the basis of some predecessors, several promising thermodynamically stable

italicAB O_{3}

‐type perovskites such as YVO 3 , SrZrO 3 , RbPaO 3 , and LaFeO 3 were reported by Chen et al 176 via an SVM regression model trained to predict the energy above the convex hull.…”

Section: Applicationmentioning

confidence: 99%

Methods, progresses, and opportunities of materials informatics

Li,

Zheng

2023

InfoMat

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As an implementation tool of data intensive scientific research methods, machine learning (ML) can effectively shorten the research and development (R&D) cycle of new materials by half or even more. ML shows great potential in the combination with other scientific research technologies, especially in the processing and classification of large amounts of material data from theoretical calculation and experimental characterization. It is very important to systematically understand the research ideas of material informatics to accelerate the exploration of new materials. Here, we provide a comprehensive introduction to the most commonly used ML modeling methods in material informatics with classic cases. Then, we review the latest progresses of prediction models, which focus on new processing–structure–properties–performance (PSPP) relationships in some popular material systems, such as perovskites, catalysts, alloys, two‐dimensional materials, and polymers. In addition, we summarize the recent pioneering researches in innovation of material research technology, such as inverse design, ML interatomic potentials, and microtopography characterization assistance, as new research directions of material informatics. Finally, we comprehensively provide the most significant challenges and outlooks related to the future innovation and development in the field of material informatics. This review provides a critical and concise appraisal for the applications of material informatics, and a systematic and coherent guidance for material scientists to choose modeling methods based on required materials and technologies.

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PSO-SVR predicting for the Ehull of ABO3-type compounds to screen the thermodynamic stable perovskite candidates based on multi-scale descriptors

Cited by 13 publications

References 25 publications

Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series

Gold Stock Price Forecasting Based on Nonlinear Weighted Particle Swarm (IPSO) Optimised Support Vector Machine (SVM) Time Series

Interpretable machine learning-assisted screening of perovskite oxides

Methods, progresses, and opportunities of materials informatics

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