OCPMDM: Online computation platform for materials data mining

Zhang, Qing; Chang, Dongping; Zhai, Xiao; Lü, Weijie

doi:10.1016/j.chemolab.2018.04.004

Cited by 31 publications

(15 citation statements)

References 34 publications

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“…In order to design new ABO 3 -type perovskites with higher SSA, the model for predicting SSAs of ABO 3 -type perovskite materials was integrated into OCPMDM, which could be used to screen out the ABO 3 -type perovskite materials with targeted properties among innumerable candidates. 17 Here the perovskite crystals of our data set, including the candidates of new ABO 3type compounds, are all cubic. The candidates to be screened using OCPMDM were designed in the following steps:…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…First, original samples of perovskites with known properties are prepared according to the published references, while the features can be automatically generated via the Online Computation Platform for Materials Data Mining (OCPMDM). 17 Next, feature selection, model selection, hyperparameter optimization, and model validation are carried out to construct the model for predicting SSAs of perovskites by using different kinds of materials data mining. Next, the available model can be used to assist experimental researchers in screening out the perovskites with higher SSA via visual high-throughput screening of perovskite candidates.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…In this work, the materials data mining was carried out using ExpMiner (a data mining software package) and OCPMDM developed in our laboratory. 17 The free version of ExpMiner can be downloaded from the website of the Laboratory of Materials Data Mining in Shanghai University (http://chemdata.shu.edu.cn:8080/ MyLab/Lab/download.jsp). OCPMDM can be accessed at http://materialdata.shu.edu.cn.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area

Shi

Chang

et al. 2018

J. Chem. Inf. Model.

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The specific surface area (SSA) of ABO3-type perovskite is one of the important properties associated with photocatalytic ability. In this work, data mining methods were used to explore the relationship between the SSA (in the range of 1–60 m2 g–1) of perovskite and its features, including chemical compositions and technical parameters. The genetic algorithm–support vector regression method was used to screen the main features for modeling. The correlation coefficient (R) between the predicted and experimental SSAs reached as high as 0.986 for the training data set and 0.935 for leave-one-out cross-validation. ABO3-type perovskites with higher SSA can be screened out using the Online Computation Platform for Materials Data Mining (OCPMDM) developed in our laboratory. Further, an online web server has been developed to share the model for the prediction of SSA of ABO3-type perovskite, which is accessible at .

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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: ■ Materials and Methodsmentioning

confidence: 99%

Section: ■ Materials and Methodsmentioning

confidence: 99%

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Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area

Shi

Chang

et al. 2018

J. Chem. Inf. Model.

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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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“…As scholarly objects, ML models should be subject to similar publication, review, and citation models on which the scholarly process is built. Lacking standard methods for doing so, (a) many models associated with published literature are not available [13]; and (b) researchers adopt a range of ad hoc methods (from customized websites to GitHub) for sharing ML models [14][15][16].…”

Section: Specialized Requirements Of Sciencementioning

confidence: 99%

DLHub: Model and Data Serving for Science

Chard

et al. 2019

2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

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While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized requirements of scientific ML. Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications. DLHub addresses two significant shortcomings in current systems. First, its selfservice model repository allows users to share, publish, verify, reproduce, and reuse models, and addresses concerns related to model reproducibility by packaging and distributing models and all constituent components. Second, it implements scalable and low-latency serving capabilities that can leverage parallel and distributed computing resources to democratize access to published models through a simple web interface. Unlike other model serving frameworks, DLHub can store and serve any Python 3compatible model or processing function, plus multiple-function pipelines. We show that relative to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, DLHub provides greater capabilities, comparable performance without memoization and batching, and significantly better performance when the latter two techniques can be employed. We also describe early uses of DLHub for scientific applications.

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OCPMDM: Online computation platform for materials data mining

Cited by 31 publications

References 34 publications

Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area

Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area

Fuel Ratio Optimization of Blast Furnace Based on Data Mining

DLHub: Model and Data Serving for Science

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