Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation

Xu, Pengcheng; Lu, Tian; Ju, Lifei; Tian, Lumin; Li, Minjie; Lü, Weijie

doi:10.1021/acs.jpcb.0c08674

Cited by 30 publications

(28 citation statements)

References 55 publications

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“…Case Study 6.1. Polymer Discovery with Targeted Band Gap Based on DFT Computation [106] Band gap is one of the most important indicators for evaluating the electrical properties of polymer materials, directly determining the voltage resistance and maximum operating temperature of polymer material devices. Polymers with appropriate band gap values could be applied in electroluminescence and polymer solar cells.…”

Section: Recent Progress Of Machine Learning In Polymersmentioning

confidence: 99%

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Chen

et al. 2022

Advcd Theory and Sims

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Under the guidance of the material genome initiative (MGI), the use of data‐driven methods to discover new materials has become an innovation of materials science. The polymer materials have been one of the most important parts in materials science for the excellent physical and chemical properties as well as corresponding complex structures. Machine learning, as the core of data‐driven methods, has taken an important place in polymer materials design and discovery. In this review, the authors have introduced the applications of machine learning in the design and discovery of polymer materials. The development tendency of published papers about machine learning in polymer materials, the commonly used algorithms, the polymer descriptors, the workflow of machine learning in polymer materials, and recent progresses of machine learning in materials are summarized. Then, the detail of how to use machine learning to assist design and discovery of polymer materials is fully discussed combined with two cases. Finally, the opportunities and challenges on the future development prospects of machine learning in the field of polymer materials are proposed.

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Section: Recent Progress Of Machine Learning In Polymersmentioning

confidence: 99%

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Chen

et al. 2022

Advcd Theory and Sims

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nondominated sorting genetic algorithm-II is used with algorithms that help achieve an optimum solution with the minimal false-positive rate (FPR) and minimal false-negative rate (FNR) to select the perfect mix of event and nonevent information (FNR) ). [17] estimates the polymer bandgap, and researchers used a machine learning frameworktermed as support vector regression (SVR), which used training data from DFT computation and Dragon to produce descriptors. The SVR design utilizing 16 important characteristics as inputs performed optimally for predicting polymer band gaps after feature selection with the greatest relevance and least redundancy.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology

Zhang

Zhou

2022

Wireless Communications and Mobile Computing

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Chemical enterprises are presently confronted with several difficult issues, including high power consumption, dangerous risk evaluation, and environmental regulation, all of which push industrial and academic institutions to develop new technologies, catalysts, and materials. Chlorinated polyethylene (CPE) is a polymer made by replacing H2 molecules in high density-(C2H4)n with chloride ions. CPE elastomers are made from a high density-(C2H4) backbone, and it was chlorinated using a free radical aqueous slurry technique. However, such fundamental polymer characteristics are insufficient to explain the performance characteristics of chlorinated polyethylene elastomers. Artificial intelligence (AI) has had a massive effect on all sections of the chemical sector, with tremendous potential that has revolutionized value supply chains, enhanced efficiency, and opened up new ways to the marketplace. As a result, in this research, we offer a methodology for the performance characterization of chlorinated polyethylene based on artificial intelligence (AI) and wireless network technology. The AI tools can search through enormous databases of known compounds and their attributes, leveraging the data to generate new possibilities. The dataset is first gathered. The chemical characterization is classified using the K -nearest neighbor (KNN) technique. This program was created to examine molecule structures and forecast the outcomes of new chemical reactions. Bayesian optimization is used to improve characterization performance. The proposed method will contribute to the future usage of AI in the chemical sector.

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“…determine directly solar cell performance. Machine learning is now more and more often used to help design better solar cells and specifically materials for solar cells and other optoelectronic applications [12][13][14][15]65,66]. It is used to predict better active materials, to optimize device performance or even optimize fabrication processes [15].…”

Section: Examples Of Input-output Mappings Used In ML For Energy Tech...mentioning

confidence: 99%

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies

Ihara¹,

Manzhos²

2022

Preprint

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Machine learning (ML) has found increasing use in research on energy conversion and storage technologies, in particular, so-called sustainable technologies. While often ML is used to directly optimize the parameters or phenomena of interest in the space of features, in this perspective, we focus on using ML to construct objects and methods that help in or enable the modeling of the underlying phenomena. We highlight the need for machine learning from very sparse and unevenly distributed numeric data in multidimensional spaces in these applications. After a brief introduction of some common regression-type machine learning techniques, we will focus on more advanced ML techniques which use these known methods as building blocks of more complex schemes and thereby allow working with extremely sparse data and also allow generating insight. Specifically, we will highlight the utility of using representations with sub-dimensional functions by combining the high-dimensional model representation ansatz with machine learning methods like neural networks or Gaussian process regressions in applications ranging from heterogeneous catalysis to nuclear energy.

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Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation

Cited by 30 publications

References 55 publications

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Evaluation of Performance of Chlorinated Polyethylene Using Wireless Network and Artificial Intelligence Technology

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies

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