Data-Mining for Processes in Chemistry, Materials, and Engineering

Li, Hao; Zhang, Zhien; Zhao, Zhe-Ze

doi:10.3390/pr7030151

Cited by 39 publications

(13 citation statements)

References 79 publications

(78 reference statements)

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“…Conventional publications are an essential part of any CompChem knowledge base, and ML is becoming useful at accelerating information extraction from the scientific literature via text mining. 532 − 534 This topic was previously comprehensively reviewed in the context of cheminformatics. 535 , 536 Natural language processing has already driven text-mining efforts for materials science discovery 535 and experimental synthesis conditions of oxides.…”

Section: Applications Of Machine Learning To Chemical Systemsmentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

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Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

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Section: Applications Of Machine Learning To Chemical Systemsmentioning

confidence: 99%

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

et al. 2021

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“…Data mining for spatial form recognition is the method of determining interesting information, for instance, configurations, relationships, modifications, irregularities, and major organisations, from big volumes of data stocked in data servers or other data sources [49]. Because of the disposal of colossal quantities of records, data mining has attracted important consideration in the information management business.…”

Section: Imagery Treatment and Prediction In Mappingmentioning

confidence: 99%

Remote Sensing: An Advanced Technique for Crop Condition Assessment

Ennouri

Kallel

2019

Mathematical Problems in Engineering

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Actually, cultivators are increasingly arranging innovative high technical and scientific estimations in the aim to enhance agricultural sustainability, effectiveness, and/or plant health. Innovative farming technologies incorporate biology with smart agriculture: computers and devices exchange with one another autonomously in a structured farm management system. Throughout this structure, smart agriculture can be accomplished; cultivators decrease plantation inputs (pesticides and fertilizers) and increase yields via integrated pest management and/or biological control. The emerging concept of remote sensing may provide a framework to systematically consider these issues of smart farming technology and to embed high-tech agriculture better. The impact(s) may be beneficial depending on how tools, such as data mining, and imagery technologies, such as picture treatment and analysis, are applied. Remote sensing technology is discussed in this review and demonstrates its possibility to create novel opportunities for scientists (and agronomists) to explore aspects of biological phenomena that cannot be accessed through usual mechanisms or processes.

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“…The mathematical model can reconstruct the nonlinear deflection value under different loading states [21][22] .In recent years, with the continuous development of machine learning, artificial neural networks have been widely used to solve problems encountered in the development of materials engineering. Under the specific mapping relationship between ambiguous data, artificial neural networks propose a nonlinear mathematical form that can accurately predict data trends 23 . BP neural network is a typical algorithm in artificial intelligence networks .The BP neural network has a simple topology with high error precision, programming ease, strong operability and wide applications 24 .…”

Section: Introductionmentioning

confidence: 99%

Strain-deformation Reconstruction of Carbon Fiber Composite Laminates Based on BP Neural Network

et al. 2019

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The Carbon Fiber Reinforced Polymer (CFRP) laminate structural components used in the aerospace and military domains require high precision and strong stability. Usually the deformation of these structural components is difficult to be measured directly during operation, but the deformation of the CFRP laminate structure can be reconstructed with strain information. The CFRP laminate structure can be designed to adapt to the requirements of different applications through layering of variable thickness. In this paper, aiming at the discontinuous stiffness and strength of the variable laminations within the CFRP laminate structure, the BP neural network is proposed to be applied to the deformation reconstruction of CFRP laminates. With strain as input and deformation as output, based on a large amount of experimental data, the BP neural network model between strain and deformation is obtained through training. In this paper, CFRP test piecs with equal thickness and variable thickness were designed, and the corresponding strain-deformation reconstruction experimental system was constructed. The strain on the surface of CFRP test piece was measured by the fiber grating sensor, and the deformation of the test piece was measured by the laser displacement sensor. The comparative analysis between the predicted deflection obtained by neural network reconstruction and the actual measured deflection shows that BP neural network can reconstruct the structural deformation of CFRP laminates within certain error range.

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Data-Mining for Processes in Chemistry, Materials, and Engineering

Cited by 39 publications

References 79 publications

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Remote Sensing: An Advanced Technique for Crop Condition Assessment

Strain-deformation Reconstruction of Carbon Fiber Composite Laminates Based on BP Neural Network

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