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RESEARCH PAPER · Journal of Materiomics · 2020

Metaheuristic-based inverse design of materials – A survey

Thunshun Warren Liao, Guoqiang Li

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Metaheuristic-based inverse design of materials – A surveyOriginalGOAMLP: Network Intrusion Detection With Multilayer Perceptron and Grasshopper Optimization Algorithm55Integrating Physics-Based Modeling with Machine Learning: A Survey285Appropriate search techniques to estimate Weibull function parameters in a Pinus spp. plantation8Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization40Physics based meta heuristics in manufacturing9HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems50Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials27Paradigm Shift Through the Integration of Physical Methodology and Data Science0Optimisation of CaO-Al2O3-SiO2-CaSO4-based catalysts performance for methanolysis of waste lard for biodiesel production using response surface methodology and meta-heuristic algorithms25Targeted design of advanced electrocatalysts by machine learning110Advancing statistical learning and artificial intelligence in nanophotonics inverse design49A hybrid science‐guided machine learning approach for modeling chemical processes: A review179Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems878Innovative methods and research directions in the field of logistics3Towards the Estimation of Body Weight in Sheep Using Metaheuristic Algorithms from Biometric Parameters in Microsystems11Structure-embedding network for predicting the transmission spectrum of a multilayer deep etched grating6Machine learning accelerates the materials discovery142MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning5The Importance and Applicability of Metaheuristics in Supply Chains0Uncertainty modeling and applications for operating data-driven inverse design7Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions45Optimization of capacitance in supercapacitors by constructing an experimentally validated hybrid artificial neural networks-genetic algorithm framework18End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design10A Hybrid Science‐Guided Machine Learning Approach for Modeling Chemical and Polymer Processes0Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains6PP-GNN: Pretraining Position-aware Graph Neural Networks with the NP-hard metric dimension problem4Presenting SLAMD – A Sequential Learning Based Software for the Inverse Design of Sustainable Cementitious Materials5Deep neural networks with adaptive solution space for inverse design of multilayer deep-etched grating13Enhancing crystal structure prediction by combining computational and experimental data via graph networks0Inverse design of experimentally synthesizable crystal structures by leveraging computational and experimental data7Dynamic exploration–exploitation trade-off in active learning regression with Bayesian hierarchical modeling5An automated design framework for composite mechanical metamaterials and its application to 2D pentamode materials11Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies33XtalOpt version 13: Multi-objective evolutionary search for novel functional materials14Review of the metaheuristic algorithms in applications: Visual analysis based on bibliometrics100Improved prediction accuracy for compressive strength of recycled aggregate concrete using optimization-based algorithms and cascade forward neural network6Transformer-Driven Inverse Learning for AI-Powered Ceramic Material Innovation With Advanced Data Preprocessing3Novel inverse predictive system integrated with industrial lubricant information3Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement9Evolution of metaheuristic algorithms in materials science2Machine Learning-Based Methods for Materials Inverse Design: A Review34Hierarchical design of mechanical metamaterials: an application on pentamode-like structures18Evolutionary artificial intelligence for 3D microstructure reconstruction based on inverse pole figure images2Reinforcement learning enhanced evolutionary algorithms for inverse design of electric cargo truck frames2Active Learning‐Guided Polymorph Control in Co‐Precipitation Synthesis1Crashworthiness Optimization of Composite/Metal Hybrid Tubes with Triggering Holes0

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  3. Appropriate search techniques to estimate Weibull function parameters in a Pinus spp. plantation

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  4. Inverse design of composite metal oxide optical materials based on deep transfer learning and global optimization

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  5. Physics based meta heuristics in manufacturing

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  6. HSCWMA: A New Hybrid SCA-WMA Algorithm for Solving Optimization Problems

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  7. Learning with Delayed Rewards—A Case Study on Inverse Defect Design in 2D Materials

    ACS Applied Materials & Interfaces · 2021-07-21

    Suvo Banik, Troy D. Loeffler, Rohit Batra, Harpal Singh, Mathew J. Cherukara, Subramanian K. R. S. Sankaranarayanan

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  8. Paradigm Shift Through the Integration of Physical Methodology and Data Science

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  9. Optimisation of CaO-Al2O3-SiO2-CaSO4-based catalysts performance for methanolysis of waste lard for biodiesel production using response surface methodology and meta-heuristic algorithms

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  10. Targeted design of advanced electrocatalysts by machine learning

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  11. Advancing statistical learning and artificial intelligence in nanophotonics inverse design

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  12. A hybrid science‐guided machine learning approach for modeling chemical processes: A review

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  14. Innovative methods and research directions in the field of logistics

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  15. Towards the Estimation of Body Weight in Sheep Using Metaheuristic Algorithms from Biometric Parameters in Microsystems

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  16. Structure-embedding network for predicting the transmission spectrum of a multilayer deep etched grating

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  17. Machine learning accelerates the materials discovery

    Materials Today Communications · 2022-11-09

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  18. MatFlow: A System for Knowledge-based Novel Materials Design using Machine Learning

    IEEE International Conference on Big Data (BigData) · 2022-12-17

    Hasan M. Jamil, Lan Li, Amin Mirkouei

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  19. The Importance and Applicability of Metaheuristics in Supply Chains

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  20. Uncertainty modeling and applications for operating data-driven inverse design

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  21. Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions

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  22. Optimization of capacitance in supercapacitors by constructing an experimentally validated hybrid artificial neural networks-genetic algorithm framework

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  23. End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

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  24. A Hybrid Science‐Guided Machine Learning Approach for Modeling Chemical and Polymer Processes

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  25. Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains

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  26. PP-GNN: Pretraining Position-aware Graph Neural Networks with the NP-hard metric dimension problem

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  27. Presenting SLAMD – A Sequential Learning Based Software for the Inverse Design of Sustainable Cementitious Materials

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  28. Deep neural networks with adaptive solution space for inverse design of multilayer deep-etched grating

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  29. Enhancing crystal structure prediction by combining computational and experimental data via graph networks

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  30. Inverse design of experimentally synthesizable crystal structures by leveraging computational and experimental data

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  36. Improved prediction accuracy for compressive strength of recycled aggregate concrete using optimization-based algorithms and cascade forward neural network

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  37. Transformer-Driven Inverse Learning for AI-Powered Ceramic Material Innovation With Advanced Data Preprocessing

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  38. Novel inverse predictive system integrated with industrial lubricant information

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  39. Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement

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  40. Evolution of metaheuristic algorithms in materials science

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  41. Machine Learning-Based Methods for Materials Inverse Design: A Review

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  44. Reinforcement learning enhanced evolutionary algorithms for inverse design of electric cargo truck frames

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