Application of machine learning for advanced material prediction and design

In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental conditions limit the experiments. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to...

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Published inEcoMat (Beijing, China) Vol. 4; no. 4
Main Authors Chan, Cheuk Hei, Sun, Mingzi, Huang, Bolong
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.07.2022
Wiley
Subjects
Accuracy
Algorithms
Artificial intelligence
Computer applications
Data collection
Data mining
Datasets
Efficiency
Experiments
Learning algorithms
Machine learning
Material properties
materials science
new structure design
Optimization
Pattern recognition systems
Predictions
property predictions
Science
Simulation
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Abstract In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental conditions limit the experiments. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary wasting of time and resources. As a data‐driven method, machine learning provides reliable and accurate performance to solve problems in material science. This review first outlines the fundamental information of machine learning. It continues with the research concerning the prediction of various properties of materials by machine learning. Then it discusses the methods for the discovery of new materials and the prediction of their structural information. Finally, we summarize other applications of machine learning in material science. This review will be beneficial for future application of machine learning in more material science research. Due to the low computational cost and short development cycle, machine learning has been widely used in material detection, material analysis, and material design. In this review, the background of machine learning has been systematically introduced and their applications in materials science from both macroscopic and microscopic perspectives has been summarized.
AbstractList In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental conditions limit the experiments. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary wasting of time and resources. As a data‐driven method, machine learning provides reliable and accurate performance to solve problems in material science. This review first outlines the fundamental information of machine learning. It continues with the research concerning the prediction of various properties of materials by machine learning. Then it discusses the methods for the discovery of new materials and the prediction of their structural information. Finally, we summarize other applications of machine learning in material science. This review will be beneficial for future application of machine learning in more material science research. image
In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental conditions limit the experiments. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary wasting of time and resources. As a data‐driven method, machine learning provides reliable and accurate performance to solve problems in material science. This review first outlines the fundamental information of machine learning. It continues with the research concerning the prediction of various properties of materials by machine learning. Then it discusses the methods for the discovery of new materials and the prediction of their structural information. Finally, we summarize other applications of machine learning in material science. This review will be beneficial for future application of machine learning in more material science research.
In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental conditions limit the experiments. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary wasting of time and resources. As a data‐driven method, machine learning provides reliable and accurate performance to solve problems in material science. This review first outlines the fundamental information of machine learning. It continues with the research concerning the prediction of various properties of materials by machine learning. Then it discusses the methods for the discovery of new materials and the prediction of their structural information. Finally, we summarize other applications of machine learning in material science. This review will be beneficial for future application of machine learning in more material science research. Due to the low computational cost and short development cycle, machine learning has been widely used in material detection, material analysis, and material design. In this review, the background of machine learning has been systematically introduced and their applications in materials science from both macroscopic and microscopic perspectives has been summarized.
Abstract In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental conditions limit the experiments. Sometimes, traditional approaches may not yield satisfactory results for the desired purpose. Therefore, it is essential to develop a new approach to accelerate experimental progress and avoid unnecessary wasting of time and resources. As a data‐driven method, machine learning provides reliable and accurate performance to solve problems in material science. This review first outlines the fundamental information of machine learning. It continues with the research concerning the prediction of various properties of materials by machine learning. Then it discusses the methods for the discovery of new materials and the prediction of their structural information. Finally, we summarize other applications of machine learning in material science. This review will be beneficial for future application of machine learning in more material science research.
Author Huang, Bolong
Chan, Cheuk Hei
Sun, Mingzi
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Snippet In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental conditions limit...
Abstract In material science, traditional experimental and computational approaches require investing enormous time and resources, and the experimental...
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SubjectTerms Accuracy
Algorithms
Artificial intelligence
Computer applications
Data collection
Data mining
Datasets
Efficiency
Experiments
Learning algorithms
Machine learning
Material properties
materials science
new structure design
Optimization
Pattern recognition systems
Predictions
property predictions
Science
Simulation
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Title Application of machine learning for advanced material prediction and design
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Volume 4
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