Prediction and analysis etching model of anti-glare glass roughness based on machine learning method DOI

Tao Yang,

Lin Zhu, Fan Yang

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 205, P. 28 - 38

Published: March 21, 2024

Language: Английский

Emerging Trends in Machine Learning: A Polymer Perspective DOI Creative Commons
Tyler B. Martin, Debra J. Audus

ACS Polymers Au, Journal Year: 2023, Volume and Issue: 3(3), P. 239 - 258

Published: Jan. 18, 2023

In the last five years, there has been tremendous growth in machine learning and artificial intelligence as applied to polymer science. Here, we highlight unique challenges presented by polymers how field is addressing them. We focus on emerging trends with an emphasis topics that have received less attention review literature. Finally, provide outlook for field, outline important areas science discuss advances from greater material community.

Language: Английский

Citations

87
In situ reductive dehalogenation of groundwater driven by innovative organic carbon source materials: Insights into the organohalide-respiratory electron transport chain DOI
Yang Yu, Yueyan Zhang, Yuqing Liu

et al.

Journal of Hazardous Materials, Journal Year: 2023, Volume and Issue: 452, P. 131243 - 131243

Published: March 21, 2023

Language: Английский

Citations

25
Machine learning for analyses and automation of structural characterization of polymer materials DOI
Shizhao Lu, Arthi Jayaraman

Progress in Polymer Science, Journal Year: 2024, Volume and Issue: 153, P. 101828 - 101828

Published: May 3, 2024

Language: Английский

Citations

12
Challenges and Strategies for Synthesizing High Performance Micro and Nanoscale High Entropy Oxide Materials DOI
Liang Zhang,

Jiru Jia,

Jianhua Yan

et al.

Small, Journal Year: 2024, Volume and Issue: 20(28)

Published: Feb. 13, 2024

Abstract High‐entropy oxide micro/nano materials (HEO MNMs) have shown broad application prospects and become hot in recent years. This review comprehensively provides an overview of the latest developments covers key aspects HEO MNMs, by discussing design principles, computer‐aided structural design, synthesis challenges strategies, as well areas. The analysis process includes role high‐throughput large‐scale HEOs along with effects temperature elevation undercooling on formation MNMs. Additionally, article summarizes high‐precision situ characterization devices field offering robust support for related research. Finally, a brief introduction to main applications MNMs is provided, emphasizing their performances. offers valuable guidance future research outlining critical issues current field.

Language: Английский

Citations

9
Multi-objective optimization in machine learning assisted materials design and discovery DOI Open Access
Pengcheng Xu, Yingying Ma, Wencong Lu

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(2)

Published: March 24, 2025

Over the past decades, machine learning has kept playing an important role in materials design and discovery. In practical applications, usually need to fulfill requirements of multiple target properties. Therefore, multi-objective optimization based on become one most promising directions. This review aims provide a detailed discussion learning-assisted discovery combined with recent research progress. First, we briefly introduce workflow learning. Then, Pareto fronts corresponding algorithms are summarized. Next, strategies demonstrated, including front-based strategy, scalarization function, constraint method. Subsequently, progress is summarized different discussed. Finally, propose future directions for learning-based materials.

Language: Английский

Citations

1
Towards defect-free lattice structures in additive manufacturing: A holistic review of machine learning advancements DOI
Numan Khan,

Hamid Asad,

Sikandar Khan

et al.

Journal of Manufacturing Processes, Journal Year: 2025, Volume and Issue: 144, P. 1 - 53

Published: April 15, 2025

Language: Английский

Citations

1
Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation DOI Creative Commons

Raghav Dangayach,

Nohyeong Jeong, Elif Demirel

et al.

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 16, 2024

Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility high tunability. Traditional trial-and-error methods material synthesis are inadequate to meet growing demands high-performance membranes. Machine learning (ML) has demonstrated huge potential accelerate design discovery membrane materials. In this review, we cover strengths weaknesses traditional methods, followed by a discussion on emergence ML developing advanced polymeric We describe methodologies data collection, preparation, commonly models, explainable artificial intelligence (XAI) tools implemented research. Furthermore, explain experimental computational validation steps verify results provided these models. Subsequently, showcase successful case studies emphasize inverse methodology within ML-driven structured framework. Finally, conclude highlighting recent progress, challenges, future research directions advance next generation With aim provide comprehensive guideline researchers, scientists, engineers assisting implementation process.

Language: Английский

Citations

6
Recursive Elimination of “Outliers” to Get Benchmark Dataset DOI Creative Commons

Langsha Liu,

Chunhui Xie, Wensheng Hu

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 98319 - 98325

Published: Jan. 1, 2024

Benchmark datasets normally have relatively conserved relationships and low fraction of outliers, indicated from higher determination coefficient (R2) lower Mean Absolute Error (MAE) in regression model. Here inspired by the process peeling onions, we introduced a recursive data elimination (RDE) "outliers" strategy to get benchmark dataset. Outliers are labeled using William's plot residual vs leverage (recorded as RDE_W), performance was compared with that alone RDE). The validation performed single-target multiple-target ways through predictions mechanical properties including Young's modulus, tensile strength, elongation at break for 643 polyurethane elastomers (the first time this dataset has been released), compressive strength 1030 concrete samples. In way, RDE_W achieved an 8.06% increase R2 19.87% reduction MAE RDE. way improvement approximately 3%. SVM outperformed XGB, NN, RF, Lasso DT algorithms strategy. Additional tests also validated advantages over RDE generate high-quality datasets. We released code facilitate construction high quality development new approaches better understand, explore design advanced materials.

Language: Английский

Citations

5
Nano zero valent iron in the 21st century: A data-driven visualization and analysis of research topics and trends DOI
Keteng Li, Jialing Li,

Fanzhi Qin

et al.

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 415, P. 137812 - 137812

Published: June 15, 2023

Language: Английский

Citations

13
Explainable molecular simulation and machine learning for carbon dioxide adsorption on magnesium oxide DOI
Honglei Yu,

Dexi Wang,

Yunlong Li

et al.

Fuel, Journal Year: 2023, Volume and Issue: 357, P. 129725 - 129725

Published: Sept. 8, 2023

Language: Английский

Citations

13