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

Shi, Li; Chang, Dongping; Ji, Xiaobo; Lu, Wei

doi:10.1021/acs.jcim.8b00436

Cited by 35 publications

(37 citation statements)

References 39 publications

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“…The network model enables more users to predict target properties. For example, Shi et al 37 developed the online server for predicting the specific surface area of ABO 3 perovskites. Furmanchuk et al 38 developed an online application to predict the Seebeck coefficient of crystalline materials.…”

Section: Model Applicationmentioning

confidence: 99%

“…ABO 3 perovskite has been widely applied as the photocatalyst or photocatalytic active component in photocatalytic reactions. Shi Li et al 37 used GA and SVM algorithms to explore the relationship between the SSA of perovskites and the composition as well as experimental conditions. After virtual screening with the developed model, five visual perovskites with larger SSA and photocatalytic potential were proposed.…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

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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: Model Applicationmentioning

confidence: 99%

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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“…[11] Shi et al developed machine learning models to predict specific surface area (SSA) of ABO3-type perovskite so that users can search for additional perovskite materials with high SSA using their model. [12] Hachmann et al also built a highly diverse database for designing the next generation of organic photovoltaics and understanding the structure-property relationship in the domain of organic electronics. [13] These data-driven machine learning approaches can potentially help the rapid screening of materials with properties of interest and provide useful guidance for de novo material design.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Polymer Representations via Quantifying Structure-Property Relationships

Ma¹,

Liu²,

Zhang³

et al. 2019

Preprint

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Machine learning techniques are being applied in quantifying structure-property relationships for a wide variety of materials, where the properly representing materials plays key roles. Although algorithms for representation learning are extensively studied, their applications to domain-specific areas, such as polymer, are limited largely due to the lack of benchmark databases. In this work, we investigate different types of polymer representations, including Morgan Fingerprint (MF), molecular embedding (ME) and molecular graph (MG), based on a benchmark database from a subset of PolyInfo. We evaluate the quality of different polymer representations via quantifying the relationships between the representations and polymer properties, including density, melting temperature and glass transition temperature. Different representation learning schemes, such as supervised learning, semi-supervised learning and transfer learning, are investigated. It is found that ME outperforms the other representations for structure-property relationship quantification in all cases studied, and MG is shown to be much inferior than ME and MF, likely due to the relatively small volumes of training data available. For MEs, it is found that the similarities of substructure MEs under different learning schemes (e.g., SL, SSL and TL) are differently estimated, thus leading to different performance scores in structure-property relation quantification. Several ME mixtures have shown to outperform the single MEs in the corresponding regression tasks, and this is attributed to the information gain when mixing different ME.

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“…The smaller the  value, the better the convergence. It can be seen that the designed resource filtering model is robust and convergent [19][20].…”

Section: { ( )mentioning

confidence: 96%

Negative Information Filtering Algorithm based on Text Content in Multimedia Networks

Chen¹,

Fu²

2019

Int J Performability Eng

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In the multimedia network environment, it is necessary to effectively filter negative information in the multimedia network and enhance the ability to mine and identify valid data. This paper presents a new algorithm of negative information filtering based on text content in multimedia networks. The principal component features of negative information in multimedia networks are extracted, and matched filters are designed to filter the negative information reasonably. All text contents and negative information are normalized and sorted. They are transformed into the same text format for classification and processing, and the filtering and detection of negative information are realized. Finally, based on the semantic features of text content, the support vector machine algorithm is used to extract negative information features from data. Experimental results show that the algorithm improves the filtering accuracy and performance for negative information in multimedia networks, and it has good application value.

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

Cited by 35 publications

References 39 publications

Machine learning for perovskite materials design and discovery

Machine learning for perovskite materials design and discovery

Evaluating Polymer Representations via Quantifying Structure-Property Relationships

Negative Information Filtering Algorithm based on Text Content in Multimedia Networks

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